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<journal-id journal-id-type="publisher-id">Front. Chem.</journal-id>
<journal-title>Frontiers in Chemistry</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Chem.</abbrev-journal-title>
<issn pub-type="epub">2296-2646</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="publisher-id">1408740</article-id>
<article-id pub-id-type="doi">10.3389/fchem.2024.1408740</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Chemistry</subject>
<subj-group>
<subject>Mini Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>The recent advances in the approach of artificial intelligence (AI) towards drug discovery</article-title>
<alt-title alt-title-type="left-running-head">Khan et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fchem.2024.1408740">10.3389/fchem.2024.1408740</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Khan</surname>
<given-names>Mahroza Kanwal</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2702940/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Raza</surname>
<given-names>Mohsin</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2702358/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Shahbaz</surname>
<given-names>Muhammad</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2708861/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Hussain</surname>
<given-names>Iftikhar</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Khan</surname>
<given-names>Muhammad Farooq</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xie</surname>
<given-names>Zhongjian</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Shah</surname>
<given-names>Syed Shoaib Ahmad</given-names>
</name>
<xref ref-type="aff" rid="aff7">
<sup>7</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tareen</surname>
<given-names>Ayesha Khan</given-names>
</name>
<xref ref-type="aff" rid="aff8">
<sup>8</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/848023/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Bashir</surname>
<given-names>Zoobia</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/formal analysis/"/>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Khan</surname>
<given-names>Karim</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/720830/overview"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>College of Chemistry and Environmental Engineering</institution>, <institution>Shenzhen University</institution>, <addr-line>Shenzhen</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Additive Manufacturing Institute</institution>, <institution>Shenzhen University</institution>, <addr-line>Shenzhen</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Department of Mechanical Engineering</institution>, <institution>City University of Hong Kong</institution>, <addr-line>Kowloon</addr-line>, <country>Hong Kong SAR, China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering</institution>, <institution>Drexel University</institution>, <addr-line>Philadelphia</addr-line>, <addr-line>PA</addr-line>, <country>United States</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Department of Electrical Engineering</institution>, <institution>Sejong University</institution>, <addr-line>Seoul</addr-line>, <country>Republic of Korea</country>
</aff>
<aff id="aff6">
<sup>6</sup>
<institution>Shenzhen Children&#x2019;s Hospital</institution>, <institution>Clinical Medical College of Southern University of Science and Technology</institution>, <addr-line>Shenzhen</addr-line>, <country>China</country>
</aff>
<aff id="aff7">
<sup>7</sup>
<institution>Department of Chemistry</institution>, <institution>School of Natural Sciences</institution>, <institution>National University of Sciences and Technology</institution>, <addr-line>Islamabad</addr-line>, <country>Pakistan</country>
</aff>
<aff id="aff8">
<sup>8</sup>
<institution>School of Mechanical Engineering</institution>, <institution>Dongguan University of Technology</institution>, <addr-line>Dongguan</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2044809/overview">Chenhui Yang</ext-link>, Northwestern Polytechnical University, China</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/967984/overview">Rizwan Ur Rehman Sagar</ext-link>, Jiangxi University of Science and Technology, China</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Mohsin Raza, <email>mohsinraza514@yahoo.com</email>; Karim Khan, <email>karim_khan_niazi@yahaoo.com</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>31</day>
<month>05</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>12</volume>
<elocation-id>1408740</elocation-id>
<history>
<date date-type="received">
<day>29</day>
<month>03</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>26</day>
<month>04</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Khan, Raza, Shahbaz, Hussain, Khan, Xie, Shah, Tareen, Bashir and Khan.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Khan, Raza, Shahbaz, Hussain, Khan, Xie, Shah, Tareen, Bashir and Khan</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and <italic>denovo</italic> drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.</p>
</abstract>
<abstract abstract-type="graphical">
<title>Graphical Abstract</title>
<p>
<graphic xlink:href="FCHEM_fchem-2024-1408740_wc_abs.tif" position="anchor"/>
</p>
</abstract>
<kwd-group>
<kwd>AI</kwd>
<kwd>drug discovery</kwd>
<kwd>machine learning</kwd>
<kwd>structure-activity relationship</kwd>
<kwd>artificial intelligence</kwd>
</kwd-group>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Medicinal and Pharmaceutical Chemistry</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>It is estimated that 2.6 billion US dollars and over a decade of dedicated work are typically required in the field of drug discovery, which is notorious for its high costs, protracted timelines, and lack of success (<xref ref-type="bibr" rid="B22">Cohen et al., 2024</xref>). Several new drugs are approved, but many of these drug candidates subsequently fail. A significant precursor shift occurred in the context of drug discovery itself, enabling the rapid development of rapidly evolving artificial intelligence (AI) (<xref ref-type="bibr" rid="B104">Tripathi et al., 2024</xref>; <xref ref-type="bibr" rid="B88">Sarkar et al., 2023</xref>). Artificial intelligence has been successfully implemented into drug discovery, encompassing target protein structure identification (<xref ref-type="bibr" rid="B33">Hasselgren and Oprea, 2024</xref>), virtual screening (<xref ref-type="bibr" rid="B106">Turon et al., 2023</xref>), <italic>de novo</italic> drug design (<xref ref-type="bibr" rid="B37">Janet et al., 2023</xref>), retrosynthesis reaction prediction (<xref ref-type="bibr" rid="B113">Yan et al., 2023</xref>), bioactivity and toxicity prediction (<xref ref-type="bibr" rid="B103">Tran et al., 2023</xref>), all of which are categorized as predictive and generative processes (<xref ref-type="fig" rid="F1">Figure 1A</xref>). Computer programs designed to emulate human cognitive processes constitute AI, a scientific discipline associated with intelligent machine learning. In this process, data is acquired, systems are constructed for using that data, conclusions are drawn, self-corrections are implemented, and adjustments are made where necessary (<xref ref-type="bibr" rid="B16">Buckner, 2023</xref>; <xref ref-type="bibr" rid="B26">Damiano and Stano, 2023</xref>; <xref ref-type="bibr" rid="B77">Prasad and Kalavakolanu, 2023</xref>; <xref ref-type="bibr" rid="B82">Ratten, 2024</xref>). It is generally used for the replication of cognitive tasks performed by humans through machine learning analysis. To conduct accurate analyses and provide meaningful interpretations, the technology relies on a variety of statistical models and computational intelligence (<xref ref-type="bibr" rid="B64">Klauschen et al., 2024</xref>). The application and integration of AI technology across diverse industries have become increasingly common in recent years (<xref ref-type="bibr" rid="B5">Ahmadi, 2024</xref>). Despite challenges such as shortages of pharmacists (<xref ref-type="bibr" rid="B63">Kilonzi et al., 2024</xref>), rising operating costs (<xref ref-type="bibr" rid="B112">Yaiprasert and Hidayanto, 2024</xref>), and diminished reimbursements (<xref ref-type="bibr" rid="B75">Pham et al., 2024</xref>), pharmacies have successfully met the rising demand for prescriptions during the past quarter-century. Pharmacy has made great strides in improving its workflow efficiency, reducing operating costs, and championing safety, accuracy, and efficiency through technology (<xref ref-type="bibr" rid="B110">Wilde et al., 2024</xref>). Besides giving pharmacists more time to direct their attention to a larger patient volume, automated dispensing systems improve health outcomes significantly. Intelligent automation is playing a pivotal role in improving both patient care and the pharmaceutical industry with this fusion of AI technology and pharmacy practices (<xref ref-type="bibr" rid="B10">Anthwal et al., 2024</xref>). The drug discovery market is expected to grow rapidly with advances in artificial intelligence technologies as well as their integration into the process as shown by <xref ref-type="fig" rid="F1">Figure 1B</xref>.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>
<bold>(A)</bold> Schematic diagram representing drug development through AI, <bold>(B)</bold> Significant growth in the US AI market in drug discovery is expected between 2023 and 2032.</p>
</caption>
<graphic xlink:href="fchem-12-1408740-g001.tif"/>
</fig>
</sec>
<sec id="s2">
<title>2 Overview of artificial intelligence in drug discovery</title>
<p>Recent advances in artificial intelligence and machine learning have ushered in a new era of efficiency in drug discovery. By combining artificial intelligence with machine learning in drug discovery, new documents have been developed to address long-standing challenges associated with traditional drug discovery, and to accelerate the identification of promising drug candidates (<xref ref-type="bibr" rid="B33">Hasselgren and Oprea, 2024</xref>; <xref ref-type="bibr" rid="B80">Ramos et al., 2024</xref>). In computer science, artificial intelligence (AI) refers to the development of intelligent machines that can perform tasks usually requiring human intelligence. The role of machine learning in drug discovery involves analyzing vast datasets and deriving meaning from them using AI, a subset of machine learning (<xref ref-type="bibr" rid="B65">Kotkondawar et al., 2024</xref>).</p>
<sec id="s2-1">
<title>2.1 Predicting drug efficacy and toxicity through machine learning (ML)</title>
<p>In medicinal chemistry, an important application of artificial intelligence is to predict the efficacy and toxicity of potential drug compounds. As a result, Artificial Intelligence (AI), especially Machine Learning (ML), has emerged as one of the most effective techniques for solving these problems (<xref ref-type="bibr" rid="B8">Alhatem et al., 2024</xref>). Analyzing large datasets allows ML algorithms to identify patterns and trends not readily evident to humans. This capability speeds up the identification of not only synthetic small molecules but also new bioactive compounds while minimizing side effects, outpacing the time constraints of traditional protocols (<xref ref-type="bibr" rid="B102">Thenuwara et al., 2023</xref>). For example, deep learning (DL) algorithms trained on a dataset of known drugs can predict the activity of new drugs with a high degree of success (<xref ref-type="bibr" rid="B11">Askr et al., 2023</xref>). The use of databases of known toxic and non-toxic compounds has enabled AI to make significant contributions to the prevention of the toxicity of potential drug compounds (<xref ref-type="bibr" rid="B114">Yang and Kar, 2023</xref>).</p>
<p>In addition to finding drug&#x2013;drug interactions in patients with different diseases, AI is also essential to identifying altered or adverse reactions caused by multiple drugs being taken together for the same or different diseases (<xref ref-type="bibr" rid="B23">Creecy et al., 2024</xref>). The detection of drug interactions is based on AI methods that analyze patterns and trends in large datasets of known interactions. An ML algorithm, for instance, accurately predicts interactions of novel drug pairs (<xref ref-type="bibr" rid="B13">Atas Guvenilir and Do&#x11f;an, 2023</xref>). As part of personalized medicine, AI can identify possible interactions between drugs. As a result, it is easier to develop tailor-made treatment plans based on the characteristics of individual patients, including genetic profiles and drug responses, aligned with personalized medicine, which tailor treatments based on individual characteristics (<xref ref-type="bibr" rid="B15">Blanco-Gonzalez et al., 2023</xref>).</p>
</sec>
<sec id="s2-2">
<title>2.2 Virtual screening: a lead identification approach</title>
<p>Virtual Screening (VS) serves as a potent methodology for lead identification within the domain of AI-driven drug discovery (<xref ref-type="bibr" rid="B79">Pun et al., 2023</xref>). By using this method, millions of compounds similar to drugs or leads are computationally screened against well-characterized proteins. Docking is used to filter ligands based on their affinities for binding (<xref ref-type="bibr" rid="B21">Chisholm et al., 2023</xref>; <xref ref-type="bibr" rid="B91">Shiota et al., 2023</xref>). These computational hits are then subjected to <italic>in vitro</italic> testing. Within the realm of AI drug discovery, virtual screening falls into two primary categories: ligand-based virtual screening (LBVS) (<xref ref-type="bibr" rid="B74">Oliveira et al., 2023</xref>) and structure-based virtual screening (SBVS) (<xref ref-type="bibr" rid="B66">Kumar and Acharya, 2023</xref>). LBVS entails the analysis of biological data to differentiate inactive compounds from active ones (<xref ref-type="bibr" rid="B29">Dragan et al., 2023</xref>). A consensus pharmacophore, similarity measure, or various descriptors are then used to identify highly active scaffolds. Conversely, SBVS requires knowledge of the 3D structure of the target protein (<xref ref-type="bibr" rid="B83">Rehman et al., 2023</xref>). By using computer algorithms, a target protein is docked with a large library of drug-like compounds available commercially. The docked complex is scored using a scoring function, followed by experimental validation assays (<xref ref-type="bibr" rid="B27">DiFrancesco et al., 2023</xref>). An important function of SBVS is scoring ligands. However, unlike ligand-based approaches, the structure-based approach does not rely on pre-existing experimental data (<xref ref-type="bibr" rid="B95">Stevenson et al., 2023</xref>).</p>
</sec>
</sec>
<sec id="s3">
<title>3 Key technologies in AI&#x2013;driven drug discovery</title>
<p>In the past decay, drug discovery was a labor-intensive process based on high-throughput screening and trial-and-error experimentation. ML and NLP techniques hold promise for improving the efficiency and effectiveness of analyzing large datasets. Improve accuracy, allowing for more precise and accurate entries through machine learning (ML) and natural language processing (NLP). (<xref ref-type="bibr" rid="B92">Sim et al., 2023</xref>). The recent achievements in applying deep learning to predict drug compound efficacy demonstrate AI&#x2019;s transformative potential in this field. In addition, it has been proven that AI techniques are capable of projecting the criminal capabilities of an individual, showing the potential to interfere with the effectiveness of drug discovery and processing (<xref ref-type="bibr" rid="B114">Yang and Kar, 2023</xref>). Clearly, it is possible and research is needed on how AI can be used to create new bioactives, despite these advances and with challenges and limitations, including ethical ones. Medical advances in the future are driven in large part by artificial intelligence.</p>
<p>It refers to any computer or machine exhibiting responsiveness or intelligence, indicating human-like speed or intelligence, often called robotics or automation. Robotic systems are designed to perform complex repetitive tasks, while artificial intelligence is concerned with giving computers or machines the ability to think like humans (<xref ref-type="bibr" rid="B109">Wardat et al., 2024</xref>). As a branch of computer science, artificial intelligence (AI) aims to develop machines that can learn (<xref ref-type="bibr" rid="B86">Sanchez et al., 2024</xref>), organize (<xref ref-type="bibr" rid="B71">Nebreda et al., 2024</xref>), problem solve (<xref ref-type="bibr" rid="B86">Sanchez et al., 2024</xref>), sense like humans. (<xref ref-type="bibr" rid="B6">Akour et al., 2024</xref>), and language (<xref ref-type="bibr" rid="B93">Singh and Khatun, 2024</xref>) with similar success. In its current form, narrow AI, also known as weak AI, is designed for specialized tasks such as web search, face and voice recognition, and self-examination (<xref ref-type="bibr" rid="B101">Thangavel et al., 2024</xref>). Ultimately, the AI community wants to develop machines capable of performing all cognitive tasks better than humans, which would lead to the development of a strong or general AI.</p>
<sec id="s3-1">
<title>3.1 A fusion of quantitative structure-activity relationship (QSAR), quantitative structure-property relationship (QSPR) and structure-based modeling</title>
<p>In the ever-evolving landscape of drug design, Artificial Intelligence (AI) combined with Quantitative Structure-Activity Relationship (QSAR), Quantitative Structure-Property Relationship (QSPR), and Structure-Based, has steadily gained ground in the 50 years. QSPR has proven its worth in guiding drug discovery, having proven its potential in predicting biological action and pharmacokinetic parameters (<xref ref-type="bibr" rid="B117">Zeng et al., 2024</xref>). As shown in <xref ref-type="sec" rid="s11">Supplementary Figure S1</xref>. Traditionally reliant on simpler models, the field has progressively embraced universally applicable machine learning techniques such as support vector machines (<xref ref-type="bibr" rid="B116">Yin et al., 2024</xref>) and gradient boosting methods (<xref ref-type="bibr" rid="B18">Chellaswamy et al., 2024</xref>). Simultaneously, the resurgence of deep learning has brought forth advancements, with graph neural networks and recurrent neural networks offering automatic feature extraction capabilities (<xref ref-type="bibr" rid="B76">Philippe et al., 2024</xref>). This has made it possible to model complex molecular structures, including peptides (<xref ref-type="bibr" rid="B38">Jin and Wei, 2024</xref>) and macrocycles (<xref ref-type="bibr" rid="B72">Nguyen et al., 2024</xref>). Challenges, such as data scarcity and incomprehensibility, have sparked research into nature-inspired machine learning and active learning strategies. In structure-based modeling, the integration of deep learning architectures, inspired by computer vision, has revolutionized predictions for protein-ligand interactions (<xref ref-type="bibr" rid="B111">Xie et al., 2024</xref>). The marriage of AI with these well-established methodologies underscores a promising trajectory in drug design, with a focus on enhanced predictive accuracy and efficiency.</p>
</sec>
<sec id="s3-2">
<title>3.2 De novo drug design with artificial intelligence</title>
<p>The creation of novel molecular entities with desired pharmacological properties, known as <italic>De novo</italic> drug design, is a formidable challenge in computer-assisted drug discovery (<xref ref-type="bibr" rid="B33">Hasselgren and Oprea, 2024</xref>). The vast chemical space, estimated from 10<sup>60</sup>&#x2013;10<sup>100</sup> potential drug-like molecules, adds complexity. Traditional structure-based and ligand-based drug design methods, though pivotal in discovering small-molecule drug candidates, face limitations due to their reliance on specific templates derived from active sites or pharmacophores. The introduction of AI techniques has revolutionized <italic>de novo</italic> drug design, with models like ReLeaSE (<xref ref-type="bibr" rid="B9">Amilpur and Dasari, 2024</xref>), ChemVAE (<xref ref-type="bibr" rid="B33">Hasselgren and Oprea, 2024</xref>), Graph INVENT (<xref ref-type="bibr" rid="B115">Yao et al., 2023</xref>), and MolRNN (<xref ref-type="bibr" rid="B105">Tropsha et al., 2023</xref>) utilizing diverse molecular representations. These deep learning-based approaches accelerate the drug discovery process by exploring chemical space efficiently. Categorized as ligand-based or structure-based, these methods use rule-based or rule-free approaches (<xref ref-type="bibr" rid="B105">Tropsha et al., 2023</xref>). Rule-based methods involve construction rules, while rule-free approaches, often based on generative deep learning models, sample molecules from a learned latent molecular representation (<xref ref-type="bibr" rid="B105">Tropsha et al., 2023</xref>). These generative models, including recurrent neural networks and variation autoencoders, are praised for their efficacy in exploring chemical space. Evaluation metrics include validity, novelty, similarity to known compounds, and scaffold diversity. A promising approach combines both rule-based and rule-free methods for designing bioactive and synthesizable molecular entities (<xref ref-type="bibr" rid="B94">Sinha et al., 2023</xref>). While current studies predominantly focus on ligand-based approaches, there is growing interest in exploring structure-based generative design, especially for targeting orphan receptors and unexplored macromolecules.</p>
</sec>
<sec id="s3-3">
<title>3.3 Drug toxicity prediction</title>
<p>Prediction of drug toxicity is an essential aspect of the drug development process, with the aim of identifying and assessing the importance of potential adverse effects or adverse reactions associated with a drug in advance, when it grows further in the development pipeline. Predicting drug toxicity is important because it is critical to the safety and wellbeing of the patients who will ultimately use the drug. Predicting Drug Toxicity Traditional techniques have placed emphasis on experimental research and animal testing, which are time-consuming, expensive, and do not always accurately reflect human responses (<xref ref-type="bibr" rid="B70">Nasnodkar et al., 2023</xref>) and with advances in machine learning (ML), drug toxicity prediction is undergoing a paradigm shift. These techniques are based on large datasets, including chemical gradients (<xref ref-type="bibr" rid="B70">Nasnodkar et al., 2023</xref>), biological pathways (<xref ref-type="bibr" rid="B32">Guo et al., 2023</xref>), and includes information on known toxicity profiles (<xref ref-type="bibr" rid="B28">Dou et al., 2023</xref>). Machine learning algorithms, such as support vector machines (<xref ref-type="bibr" rid="B61">Khan et al., 2024</xref>), random forests (<xref ref-type="bibr" rid="B24">Daghighi, 2023</xref>), and neural networks (<xref ref-type="bibr" rid="B73">Noor et al., 2023</xref>), are trained on these data sets to learn patterns and relationships that identify potential toxicity.</p>
<p>The use of artificial intelligence in predicting drug toxicity offers several advantages. This enables the analysis of large data sets, allowing for a more complete understanding of the complex interactions between drugs and biological systems (<xref ref-type="bibr" rid="B70">Nasnodkar et al., 2023</xref>). Machine learning models can identify hidden patterns and consensual relationships that are not apparent through traditional techniques. In addition, these models can help to better and more quickly determine potential toxicities for new drug candidates, which helps in the drug development phase (<xref ref-type="bibr" rid="B81">Rasool et al., 2023</xref>). Yes, but challenges remain, such as the need for optimal quality, different training data, and evaluation of complex AI models. Ethical acceptance and regulatory standards also play an important role in the integration of AI-based toxicity prediction into the drug development process. Despite these challenges, there is great promise in artificial intelligence-driven drug toxicity prediction to aid the safety and success of novel pharmaceuticals (<xref ref-type="bibr" rid="B107">Vora et al., 2023</xref>). &#x201c;Continued research and collaboration between researchers, data scientists, and regulatory agencies is essential to ensure the accuracy of the prediction of eye-driven toxicity and progress in this field.</p>
</sec>
<sec id="s3-4">
<title>3.4 Integration of AI in retrosynthesis and reaction prediction</title>
<p>Retrosynthesis and reaction prediction have long been crucial in organic chemistry, guiding the planning of synthetic routes. With the intersection of material science and bioscience at the bio-interface, the advent of Computer-Assisted Organic Synthesis (CAOS) (<xref ref-type="bibr" rid="B87">Sankaranarayanan and Jensen, 2023</xref>) has emerged as a powerful tool for synthetic planning. In recent years, the exponential growth in reaction datasets and computational power has paved the way for the development of advanced machine learning (ML) and artificial intelligence (AI) models specifically tailored for CAOS programs (<xref ref-type="bibr" rid="B1">Abbasi and Rahmani, 2023</xref>). These models exhibit the capability to accurately predict individual synthetic and retrosynthetic reactions, offering valuable insights for chemists in designing synthetic pathways. One notable advancement involves combining single-step predictions through the integration of proper graph search algorithms (<xref ref-type="bibr" rid="B40">Kassa et al., 2023</xref>). This innovative approach has allowed researchers to design CAOS programs that excel in making comprehensive synthetic pathway predictions. By leveraging the wealth of data and computational capabilities, these programs contribute to the efficiency of synthetic planning, especially in the intricate domains of material and bio-interface studies. The integration of AI and ML in CAOS not only accelerates the prediction of viable synthetic routes but also enables chemists to explore complex reaction landscapes efficiently (<xref ref-type="bibr" rid="B68">L&#xf3;pez, 2023</xref>). The success of these programs lies in their ability to navigate diverse chemical spaces, providing valuable guidance for designing novel compounds at the bio-interface. However, challenges persist in ensuring the reliability of predictions, addressing issues of interpretability, and refining the algorithms for diverse chemical contexts (<xref ref-type="bibr" rid="B69">Mittal and Ahuja, 2023</xref>). Continued collaboration between computational chemists, organic chemists, and data scientists remains essential for further advancing CAOS applications. The synergy of retrosynthesis, reaction prediction, and CAOS stands as a testament to the transformative potential of AI-driven tools in shaping the future of synthetic chemistry at the interface of materials and bioscience. <xref ref-type="sec" rid="s11">Supplementary Table S1</xref> provides a concise overview of different applications of AI in the field of drug discovery, making it easier to understand the breadth of impact.</p>
</sec>
</sec>
<sec id="s4">
<title>4 Limitations of artificial intelligence</title>
<p>While artificial intelligence holds promise in drug discovery, there are significant challenges and limitations that demand careful consideration. One primary challenge is the availability of suitable data. AI-driven approaches typically rely on extensive datasets for effective training (<xref ref-type="bibr" rid="B15">Blanco-Gonzalez et al., 2023</xref>). However, in many instances, the accessible data may be limited, of suboptimal quality, or inconsistent, thereby compromising the accuracy and reliability of the results. Ethical considerations also present a challenge (<xref ref-type="bibr" rid="B78">Prem, 2023</xref>), as EI-based techniques have brought problems like fairness and biases, as discussed in the received section. For example, if the data used to train the machine learning (ML) algorithm is biased or does not properly represent the perspectives of different viewers, the unique predictions may be incorrect or invalid. Can be bent. Addressing and integrating the ethical implications of E-I is instrumental in the development of new therapeutic compounds. Different strategies can be used to meet these challenges within the scope of chemotherapy in this field. Data augmentation is a technique that involves the production of synthetic data to complement existing data sets. The amount and variety of data available for training these machine algorithms can be greatly increased, yielding and tolerating results. Other measures include the use of Explicit AI (XAI) methods, which aim to provide interpretability and transparency to the predictions of machine algorithms. Such methods contribute to addressing concerns about bias and fairness in AI-driven approaches, providing a clearer understanding of the underlying mechanisms and assumptions guiding predictions (<xref ref-type="bibr" rid="B20">Chen et al., 2023</xref>).</p>
<p>Contemporary AI-based methodologies should not be viewed as a substitute for conventional experimental approaches, and they cannot replace the valuable expertise and experience contributed by human researchers (<xref ref-type="bibr" rid="B31">Dwivedi et al., 2023</xref>). AI is limited to offering predictions based on available data, and the subsequent validation and interpretation of results still rely on human researchers. Nevertheless, the integration of AI alongside traditional experimental methods has the potential to enhance the drug discovery process. Through the synergistic combination of AI&#x2019;s predictive capabilities and the insights derived from the expertise and experience of human researchers, there exists an opportunity to optimize the drug discovery process and expedite the development of new medications (<xref ref-type="bibr" rid="B33">Hasselgren and Oprea, 2024</xref>; <xref ref-type="bibr" rid="B7">Alharbi et al., 2024</xref>; <xref ref-type="bibr" rid="B120">Zhang et al., 2024</xref>; <xref ref-type="bibr" rid="B90">Shi et al., 2022</xref>; <xref ref-type="bibr" rid="B39">Kang et al., 2020</xref>; <xref ref-type="bibr" rid="B14">Bibb&#xf2; et al., 2017</xref>; <xref ref-type="bibr" rid="B43">Khan et al., 2020a</xref>; <xref ref-type="bibr" rid="B35">Iqbal et al., 2019</xref>; <xref ref-type="bibr" rid="B59">Khan et al., 2018a</xref>; <xref ref-type="bibr" rid="B60">Khan et al., 2021a</xref>; <xref ref-type="bibr" rid="B36">Jamil et al., 2021</xref>; <xref ref-type="bibr" rid="B47">Khan et al., 2020b</xref>; <xref ref-type="bibr" rid="B98">Tareen et al., 2021a</xref>; <xref ref-type="bibr" rid="B57">Khan et al., 2023</xref>; <xref ref-type="bibr" rid="B49">Khan et al., 2020c</xref>; <xref ref-type="bibr" rid="B54">Khan et al., 2021b</xref>; <xref ref-type="bibr" rid="B100">Tareen et al., 2022a</xref>; <xref ref-type="bibr" rid="B51">Khan et al., 2019a</xref>; <xref ref-type="bibr" rid="B17">Cao et al., 2012</xref>; <xref ref-type="bibr" rid="B118">Zhang et al., 2019</xref>; <xref ref-type="bibr" rid="B34">Hu et al., 2020</xref>; <xref ref-type="bibr" rid="B99">Tareen et al., 2022b</xref>; <xref ref-type="bibr" rid="B44">Khan et al., 2019b</xref>; <xref ref-type="bibr" rid="B58">Khan et al., 2021c</xref>; <xref ref-type="bibr" rid="B55">Khan et al., 2021d</xref>; <xref ref-type="bibr" rid="B56">Khan et al., 2021e</xref>; <xref ref-type="bibr" rid="B12">Aslam et al., 2021</xref>; <xref ref-type="bibr" rid="B4">Ahmad et al., 2021a</xref>; <xref ref-type="bibr" rid="B89">Shaheen et al., 2023</xref>; <xref ref-type="bibr" rid="B67">Li et al., 2023</xref>; <xref ref-type="bibr" rid="B96">Tang et al., 2021</xref>; <xref ref-type="bibr" rid="B46">Khan et al., 2019c</xref>; <xref ref-type="bibr" rid="B45">Khan et al., 2019d</xref>; <xref ref-type="bibr" rid="B62">Khatoon et al., 2020</xref>; <xref ref-type="bibr" rid="B42">Khan et al., 2018b</xref>; <xref ref-type="bibr" rid="B50">Khan et al., 2020d</xref>; <xref ref-type="bibr" rid="B53">Khan et al., 2018c</xref>; <xref ref-type="bibr" rid="B52">Khan et al., 2018d</xref>; <xref ref-type="bibr" rid="B3">Ahmad et al., 2021b</xref>; <xref ref-type="bibr" rid="B30">Duan et al., 2023</xref>; <xref ref-type="bibr" rid="B25">Dai et al., 2018</xref>) (<xref ref-type="fig" rid="F2">Figure 2A</xref>).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>
<bold>(A)</bold> Graphical representation of the comparison between strengths and limitations of AI, <bold>(B)</bold> Strategies and Approaches to Overcome Current Challenges.</p>
</caption>
<graphic xlink:href="fchem-12-1408740-g002.tif"/>
</fig>
</sec>
<sec id="s5">
<title>5 Strategies and approaches to overcome current challenges</title>
<p>Incorporating artificial intelligence (AI) into drug discovery is a strategy of caution to overcome the current challenges. This consideration will aid in the continued development of AI in drug research. A foundational emphasis is placed on optimizing data inputs, prioritizing diverse and high-quality datasets as the bedrock for robust AI models. This addresses challenges related to data representativeness and accuracy (<xref ref-type="fig" rid="F2">Figure 2B</xref>).</p>
<p>The establishment of ethical guidelines and governance frameworks is a critical imperative, making responsible and ethical AI use a guiding principle. This encompasses considerations such as data privacy and consent. Interdisciplinary collaboration emerges as an essential strategy, bridging the expertise of AI specialists with professionals in pharmacology, chemistry, and biology. This fosters a synergistic alliance, integrating computational capabilities with domain-specific knowledge. Transparency in AI decision-making gains significance, with the integration of Explainable AI (XAI) techniques instrumental in providing a clear understanding of AI-driven insights, particularly in the nuanced landscape of drug discovery. Adaptability is a key consideration, with the development of AI systems capable of continuous learning, ensuring sustained relevance in the dynamic field of drug discovery.</p>
<p>Holistic integration of computational predictions with traditional experimental methods is proposed, enhancing the reliability of drug discovery processes by capitalizing on the strengths inherent in both methodologies. Addressing biases within AI models becomes a central focus, with rigorous evaluations and mitigation strategies imperative to promote fairness and prevent disparities in drug discovery outcomes.</p>
<p>Engagements with regulatory bodies based on principles of quality and validation are supported to enable acceptance and regulation of AI-based tools in drug discovery. The driving force behind AI research is to promote open collaboration and data sharing that creates a culture of shared growth in the area of drug discovery.</p>
<p>Finally, the recommendation for investment in education and skill development programs serves to bridge the knowledge gap, ensuring a proficient workforce capable of navigating the intersection of AI and pharmaceutical sciences. In conclusion, these strategies collectively shape a comprehensive framework for overcoming existing challenges and optimizing the role of AI in advancing drug discovery methodologies (<xref ref-type="bibr" rid="B19">Chen et al., 2021</xref>; <xref ref-type="bibr" rid="B84">Sagar et al., 2021</xref>; <xref ref-type="bibr" rid="B85">Sagar et al., 2024</xref>).</p>
</sec>
<sec id="s6">
<title>6 Conclusion and summary of the potential of AI for revolutionizing drug discovery</title>
<p>A paradigm shift in pharmaceutical research and development is being brought about by the integration of AI into drug discovery processes. With the advent of AI, drug discovery pipelines have been significantly accelerated, offering novel solutions to longstanding challenges, such as identifying target protein structures, conducting virtual screenings, designing new drugs, predicting retrosynthesis reactions, bioactivity and toxicity. The scientific community and society overall must recognize the implications of AI-driven drug discovery moving forward. In the coming years, AI will have a significant impact on the drug development process, as it can streamline processes, reduce costs, and improve the efficiency and success rate of the identification of viable drug candidates. Furthermore, AI technologies could revolutionize patient care by improving medication management and improving healthcare delivery with the integration of AI technologies into pharmacy practices. In future, it is imperative to address several key issues. It is most important to develop new methods tailored to specific drug discovery challenges and optimize existing AI algorithms. It is also essential to integrate AI into existing drug discovery workflows seamlessly and foster collaboration among researchers, industry stakeholders, and regulatory bodies to ensure that AI is used in drug development in a responsible and ethical manner. As a result, the ongoing evolution of AI in drug discovery offers great promise for transforming the pharmaceutical sector and improving global health. It is possible to develop faster and more efficiently safer, more effective medications using AI-driven innovation and collaboration.</p>
</sec>
</body>
<back>
<sec id="s7">
<title>Author contributions</title>
<p>MaK: Writing&#x2013;original draft. MR: Conceptualization, Writing&#x2013;review and editing. MS: Data curation, Writing&#x2013;review and editing. IH: Validation, Writing&#x2013;review and editing. MuK: Formal Analysis, Writing&#x2013;review and editing. ZX: Validation, Writing&#x2013;review and editing. SS: Methodology, Writing&#x2013;review and editing. AT: Methodology, Writing&#x2013;review and editing. ZB: Formal analysis, riting&#x2013;review and editing. KK: Supervision, Writing&#x2013;review and editing.</p>
</sec>
<sec sec-type="funding-information" id="s8">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s11">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fchem.2024.1408740/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fchem.2024.1408740/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="DataSheet1.docx" id="SM1" mimetype="application/docx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<ref-list>
<title>References</title>
<ref id="B1">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Abbasi</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Rahmani</surname>
<given-names>A. M.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Artificial intelligence and software modeling approaches in autonomous vehicles for safety management: a systematic review</article-title>. <source>Information</source> <volume>14</volume> (<issue>10</issue>), <fpage>555</fpage>. <pub-id pub-id-type="doi">10.3390/info14100555</pub-id>
</citation>
</ref>
<ref id="B2">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ahmad</surname>
<given-names>S. O. A.</given-names>
</name>
<name>
<surname>Ashfaq</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Akbar</surname>
<given-names>M. U.</given-names>
</name>
<name>
<surname>Ikram</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>F.</given-names>
</name>
<etal/>
</person-group> (<year>2021c</year>). <article-title>Application of two-dimensional materials in perovskite solar cells; recent progress, challenges and prospective solutions</article-title>. <source>J. Mater. Chem. C</source> <volume>9</volume>, <fpage>14065</fpage>&#x2013;<lpage>14092</lpage>. <pub-id pub-id-type="doi">10.1039/d1tc02407h</pub-id>
</citation>
</ref>
<ref id="B3">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ahmad</surname>
<given-names>W.</given-names>
</name>
<name>
<surname>Gong</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Abbas</surname>
<given-names>G.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>G.</given-names>
</name>
<etal/>
</person-group> (<year>2021b</year>). <article-title>Evolution of low-dimensional material-based field-effect transistors</article-title>. <source>Nanoscale</source> <volume>13</volume> (<issue>10</issue>), <fpage>5162</fpage>&#x2013;<lpage>5186</lpage>. <pub-id pub-id-type="doi">10.1039/d0nr07548e</pub-id>
</citation>
</ref>
<ref id="B4">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ahmad</surname>
<given-names>W.</given-names>
</name>
<name>
<surname>Ullah</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Sonil</surname>
<given-names>N. I.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
</person-group> (<year>2021a</year>). <article-title>Introduction, production, characterization and applications of defects in graphene</article-title>. <source>J. Mater. Sci. Mater. Electron.</source> <volume>32</volume>, <fpage>19991</fpage>&#x2013;<lpage>20030</lpage>. <pub-id pub-id-type="doi">10.1007/s10854-021-06575-1</pub-id>
</citation>
</ref>
<ref id="B5">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ahmadi</surname>
<given-names>S.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>A comprehensive study on integration of big data and AI in financial industry and its effect on present and future opportunities</article-title>. <source>Int. J. Curr. Sci. Res. Rev.</source> <volume>7</volume> (<issue>01</issue>). <pub-id pub-id-type="doi">10.47191/ijcsrr/v7-i1-07</pub-id>
</citation>
</ref>
<ref id="B6">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Akour</surname>
<given-names>I.</given-names>
</name>
<name>
<surname>Alzyoud</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Alquqa</surname>
<given-names>E.</given-names>
</name>
<name>
<surname>Tariq</surname>
<given-names>E.</given-names>
</name>
<name>
<surname>Alzboun</surname>
<given-names>N.</given-names>
</name>
<name>
<surname>Al-Hawary</surname>
<given-names>S.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Artificial intelligence and financial decisions: empirical evidence from developing economies</article-title>. <source>Int. J. Data Netw. Sci.</source> <volume>8</volume> (<issue>1</issue>), <fpage>101</fpage>&#x2013;<lpage>108</lpage>. <pub-id pub-id-type="doi">10.5267/j.ijdns.2023.10.013</pub-id>
</citation>
</ref>
<ref id="B7">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Alharbi</surname>
<given-names>H. F.</given-names>
</name>
<name>
<surname>Bhupathyraaj</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Mohandoss</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Chacko</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Rani</surname>
<given-names>K. R. V.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>An overview of artificial intelligence-driven pharmaceutical functionality</article-title>. <source>Artif. Intell. Pharm. Sci.</source>, <fpage>18</fpage>&#x2013;<lpage>36</lpage>. <pub-id pub-id-type="doi">10.1201/9781003343981-2</pub-id>
</citation>
</ref>
<ref id="B8">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Alhatem</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Wong</surname>
<given-names>T.</given-names>
</name>
<name>
<surname>Lambert</surname>
<given-names>W. C.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Revolutionizing diagnostic pathology: the emergence and impact of artificial intelligence-what doesn&#x27;t kill you makes you stronger?</article-title> <source>Clin. Dermatology</source>. <pub-id pub-id-type="doi">10.1016/j.clindermatol.2023.12.020</pub-id>
</citation>
</ref>
<ref id="B9">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Amilpur</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Dasari</surname>
<given-names>C. M.</given-names>
</name>
</person-group> (<year>2024</year>). &#x201c;<article-title>20 DN-based to identify DTI model potential drug molecules against COVID-19</article-title>,&#x201d; in <source>Handbook of AI-based Models in Healthcare and medicine: approaches, theories, and applications</source>, <fpage>397</fpage>.</citation>
</ref>
<ref id="B10">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Anthwal</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Uniyal</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Gairolla</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Singh</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Gehlot</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Abbas</surname>
<given-names>M.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Industry 4.0 technologies adoption for digital transition in drug discovery and development: a review</article-title>. <source>J. Industrial Inf. Integration</source> <volume>38</volume>, <fpage>100562</fpage>. <pub-id pub-id-type="doi">10.1016/j.jii.2024.100562</pub-id>
</citation>
</ref>
<ref id="B11">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Askr</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Elgeldawi</surname>
<given-names>E.</given-names>
</name>
<name>
<surname>Aboul Ella</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Elshaier</surname>
<given-names>Y. A.</given-names>
</name>
<name>
<surname>Gomaa</surname>
<given-names>M. M.</given-names>
</name>
<name>
<surname>Hassanien</surname>
<given-names>A. E.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Deep learning in drug discovery: an integrative review and future challenges</article-title>. <source>Artif. Intell. Rev.</source> <volume>56</volume> (<issue>7</issue>), <fpage>5975</fpage>&#x2013;<lpage>6037</lpage>. <pub-id pub-id-type="doi">10.1007/s10462-022-10306-1</pub-id>
</citation>
</ref>
<ref id="B12">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Aslam</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Sagar</surname>
<given-names>R. U. R.</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Gbadamasi</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Butt</surname>
<given-names>S.</given-names>
</name>
<etal/>
</person-group> (<year>2021</year>). <article-title>Mixed-dimensional niobium disulfide-graphene foam heterostructures as an efficient catalyst for hydrogen production</article-title>. <source>Int. J. Hydrogen Energy</source> <volume>46</volume> (<issue>68</issue>), <fpage>33679</fpage>&#x2013;<lpage>33688</lpage>. <pub-id pub-id-type="doi">10.1016/j.ijhydene.2021.07.170</pub-id>
</citation>
</ref>
<ref id="B13">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Atas Guvenilir</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Do&#x11f;an</surname>
<given-names>T.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>How to approach machine learning-based prediction of drug/compound&#x2013;target interactions</article-title>. <source>J. Cheminformatics</source> <volume>15</volume> (<issue>1</issue>), <fpage>16</fpage>&#x2013;<lpage>36</lpage>. <pub-id pub-id-type="doi">10.1186/s13321-023-00689-w</pub-id>
</citation>
</ref>
<ref id="B14">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Bibb&#xf2;</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>Q.</given-names>
</name>
<name>
<surname>Lin</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>Q.</given-names>
</name>
<name>
<surname>Ouyang</surname>
<given-names>Z.</given-names>
</name>
</person-group> (<year>2017</year>). <article-title>Tunable narrowband antireflection optical filter with a metasurface</article-title>. <source>Photonics Res.</source> <volume>5</volume> (<issue>5</issue>), <fpage>500</fpage>&#x2013;<lpage>506</lpage>. <pub-id pub-id-type="doi">10.1364/prj.5.000500</pub-id>
</citation>
</ref>
<ref id="B15">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Blanco-Gonzalez</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Cabezon</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Seco-Gonzalez</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Conde-Torres</surname>
<given-names>D.</given-names>
</name>
<name>
<surname>Antelo-Riveiro</surname>
<given-names>P.</given-names>
</name>
<name>
<surname>Pineiro</surname>
<given-names>A.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>The role of ai in drug discovery: challenges, opportunities, and strategies</article-title>. <source>Pharmaceuticals</source> <volume>16</volume> (<issue>6</issue>), <fpage>891</fpage>. <pub-id pub-id-type="doi">10.3390/ph16060891</pub-id>
</citation>
</ref>
<ref id="B16">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Buckner</surname>
<given-names>C. J.</given-names>
</name>
</person-group> (<year>2023</year>) <source>From deep learning to rational machines: what the history of philosophy can teach us about the future of artificial intelligence</source>. <publisher-name>Oxford University Press</publisher-name>.</citation>
</ref>
<ref id="B17">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Cao</surname>
<given-names>F.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Qian</surname>
<given-names>W.</given-names>
</name>
<etal/>
</person-group> (<year>2012</year>). <article-title>Recent advances in oxidation stable chemistry of two-dimensional MXenes</article-title>. <source>Adv. Mater. n/a</source>, <fpage>2107554</fpage>. <pub-id pub-id-type="doi">10.1002/adma.202107554</pub-id>
</citation>
</ref>
<ref id="B18">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Chellaswamy</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Ramasubramanian</surname>
<given-names>B.</given-names>
</name>
<name>
<surname>Sriram</surname>
<given-names>A.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>FPGA-based remote target classification in hyperspectral imaging using multi-graph neural network</article-title>. <source>Microprocess. Microsystems</source> <volume>105</volume>, <fpage>105008</fpage>. <pub-id pub-id-type="doi">10.1016/j.micpro.2024.105008</pub-id>
</citation>
</ref>
<ref id="B19">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Chen</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Sagar</surname>
<given-names>R. U. R.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Deng</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Hussain</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>W.</given-names>
</name>
<etal/>
</person-group> (<year>2021</year>). <article-title>Neodymium-decorated graphene as an efficient electrocatalyst for hydrogen production</article-title>, <source>Nanoscale</source> <volume>13</volume>, <fpage>15471</fpage>&#x2013;<lpage>15480</lpage>. <pub-id pub-id-type="doi">10.1039/d1nr03992j</pub-id>
</citation>
</ref>
<ref id="B20">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Chen</surname>
<given-names>P.</given-names>
</name>
<name>
<surname>Wu</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>L.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>AI fairness in data management and analytics: a review on challenges, methodologies and applications</article-title>. <source>Appl. Sci.</source> <volume>13</volume> (<issue>18</issue>), <fpage>10258</fpage>. <pub-id pub-id-type="doi">10.3390/app131810258</pub-id>
</citation>
</ref>
<ref id="B21">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Chisholm</surname>
<given-names>T. S.</given-names>
</name>
<name>
<surname>Mackey</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Hunter</surname>
<given-names>C. A.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Discovery of high-affinity amyloid ligands using a ligand-based virtual screening pipeline</article-title>. <source>J. Am. Chem. Soc.</source> <volume>145</volume> (<issue>29</issue>), <fpage>15936</fpage>&#x2013;<lpage>15950</lpage>. <pub-id pub-id-type="doi">10.1021/jacs.3c03749</pub-id>
</citation>
</ref>
<ref id="B22">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Cohen</surname>
<given-names>E. B.</given-names>
</name>
<name>
<surname>Patwardhan</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Raheja</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Alpers</surname>
<given-names>D. H.</given-names>
</name>
<name>
<surname>Andrade</surname>
<given-names>R. J.</given-names>
</name>
<name>
<surname>Avigan</surname>
<given-names>M. I.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Drug-Induced liver injury in the elderly: consensus statements and recommendations from the IQ-DILI initiative</article-title>. <source>Drug Saf.</source> <volume>47</volume>, <fpage>301</fpage>&#x2013;<lpage>319</lpage>. <pub-id pub-id-type="doi">10.1007/s40264-023-01390-5</pub-id>
</citation>
</ref>
<ref id="B23">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Creecy</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Awosanya</surname>
<given-names>O. D.</given-names>
</name>
<name>
<surname>Harris</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Qiao</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Ozanne</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Toepp</surname>
<given-names>A. J.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>COVID-19 and bone loss: a review of risk factors, mechanisms, and future directions</article-title>. <source>Curr. Osteoporos. Rep.</source> <volume>22</volume>, <fpage>122</fpage>&#x2013;<lpage>134</lpage>. <pub-id pub-id-type="doi">10.1007/s11914-023-00842-2</pub-id>
</citation>
</ref>
<ref id="B24">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Daghighi</surname>
<given-names>A.</given-names>
</name>
</person-group> (<year>2023</year>). in <source>In silico toxicology: application of machine learning for predicting toxicity of organic compounds</source> (<publisher-loc>Fargo, North Dakota </publisher-loc>: <publisher-name>North Dakota State University of Agriculture and Applied Science</publisher-name>).</citation>
</ref>
<ref id="B25">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Dai</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Huo</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>Q.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Shen</surname>
<given-names>C.</given-names>
</name>
</person-group> (<year>2018</year>). <article-title>Electrochemical mechanism and structure simulation of 2D lithium&#x2010;ion battery</article-title>. <source>Adv. Theory Simulations</source> <volume>1</volume> (<issue>10</issue>), <fpage>1800023</fpage>. <pub-id pub-id-type="doi">10.1002/adts.201800023</pub-id>
</citation>
</ref>
<ref id="B26">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Damiano</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Stano</surname>
<given-names>P.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Explorative synthetic biology in AI: criteria of relevance and a taxonomy for synthetic models of living and cognitive processes</article-title>. <source>Artif. Life</source> <volume>29</volume> (<issue>3</issue>), <fpage>367</fpage>&#x2013;<lpage>387</lpage>. <pub-id pub-id-type="doi">10.1162/artl_a_00411</pub-id>
</citation>
</ref>
<ref id="B27">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>DiFrancesco</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Hofer</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Aradhya</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Rufinus</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Stoddart</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Finocchiaro</surname>
<given-names>S.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Discovery of small-molecule PD-1/PD-L1 antagonists through combined virtual screening and experimental validation</article-title>. <source>Comput. Biol. Chem.</source> <volume>102</volume>, <fpage>107804</fpage>. <pub-id pub-id-type="doi">10.1016/j.compbiolchem.2022.107804</pub-id>
</citation>
</ref>
<ref id="B28">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Dou</surname>
<given-names>B.</given-names>
</name>
<name>
<surname>Zhu</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Merkurjev</surname>
<given-names>E.</given-names>
</name>
<name>
<surname>Ke</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Chen</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Jiang</surname>
<given-names>J.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Machine learning methods for small data challenges in molecular science</article-title>. <source>Chem. Rev.</source> <volume>123</volume> (<issue>13</issue>), <fpage>8736</fpage>&#x2013;<lpage>8780</lpage>. <pub-id pub-id-type="doi">10.1021/acs.chemrev.3c00189</pub-id>
</citation>
</ref>
<ref id="B29">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Dragan</surname>
<given-names>P.</given-names>
</name>
<name>
<surname>Joshi</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Atzei</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Latek</surname>
<given-names>D.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Keras/TensorFlow in drug design for immunity disorders</article-title>. <source>Int. J. Mol. Sci.</source> <volume>24</volume> (<issue>19</issue>), <fpage>15009</fpage>. <pub-id pub-id-type="doi">10.3390/ijms241915009</pub-id>
</citation>
</ref>
<ref id="B30">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Duan</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Xie</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>B.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Emerging monoelemental 2D materials (Xenes) for biosensor applications</article-title>. <source>Nano Res.</source> <volume>16</volume> (<issue>5</issue>), <fpage>7030</fpage>&#x2013;<lpage>7052</lpage>. <pub-id pub-id-type="doi">10.1007/s12274-023-5418-3</pub-id>
</citation>
</ref>
<ref id="B31">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Dwivedi</surname>
<given-names>Y. K.</given-names>
</name>
<name>
<surname>Kshetri</surname>
<given-names>N.</given-names>
</name>
<name>
<surname>Hughes</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Slade</surname>
<given-names>E. L.</given-names>
</name>
<name>
<surname>Jeyaraj</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Kar</surname>
<given-names>A. K.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>&#x201c;So what if ChatGPT wrote it?&#x201d; Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy</article-title>. <source>Int. J. Inf. Manag.</source> <volume>71</volume>, <fpage>102642</fpage>. <pub-id pub-id-type="doi">10.1016/j.ijinfomgt.2023.102642</pub-id>
</citation>
</ref>
<ref id="B32">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Guo</surname>
<given-names>W.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Dong</surname>
<given-names>F.</given-names>
</name>
<name>
<surname>Song</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Li</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>M. K. H.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Review of machine learning and deep learning models for toxicity prediction</article-title>. <source>Exp. Biol. Med.</source> <volume>248</volume>, <fpage>1952</fpage>&#x2013;<lpage>1973</lpage>. <pub-id pub-id-type="doi">10.1177/15353702231209421</pub-id>
</citation>
</ref>
<ref id="B33">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Hasselgren</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Oprea</surname>
<given-names>T. I.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Artificial intelligence for drug discovery: are we there yet?</article-title> <source>Annu. Rev. Pharmacol. Toxicol.</source> <volume>64</volume>, <fpage>527</fpage>&#x2013;<lpage>550</lpage>. <pub-id pub-id-type="doi">10.1146/annurev-pharmtox-040323-040828</pub-id>
</citation>
</ref>
<ref id="B34">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Hu</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Shi</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Cao</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Liang</surname>
<given-names>W.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<etal/>
</person-group> (<year>2020</year>). <article-title>Recent advances in doping engineering of black phosphorus</article-title>. <source>J. Mater. Chem. A</source> <volume>8</volume> (<issue>11</issue>), <fpage>5421</fpage>&#x2013;<lpage>5441</lpage>. <pub-id pub-id-type="doi">10.1039/d0ta00416b</pub-id>
</citation>
</ref>
<ref id="B35">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Iqbal</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Ahmad</surname>
<given-names>K. S.</given-names>
</name>
<name>
<surname>Rana</surname>
<given-names>F. M.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<etal/>
</person-group> (<year>2019</year>). <article-title>Synthesis and characterization of transition metals doped CuO nanostructure and their application in hybrid bulk heterojunction solar cells</article-title>. <source>SN Appl. Sci.</source> <volume>1</volume> (<issue>6</issue>), <fpage>647</fpage>. <pub-id pub-id-type="doi">10.1007/s42452-019-0663-5</pub-id>
</citation>
</ref>
<ref id="B36">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Jamil</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Loomba</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Xian</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Yousaf</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<etal/>
</person-group> (<year>2021</year>) <source>The role of nitrogen in transition-metal nitrides in electrochemical water splitting</source>.</citation>
</ref>
<ref id="B37">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Janet</surname>
<given-names>J. P.</given-names>
</name>
<name>
<surname>Mervin</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Engkvist</surname>
<given-names>O.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Artificial intelligence in molecular <italic>de novo</italic> design: integration with experiment</article-title>. <source>Curr. Opin. Struct. Biol.</source> <volume>80</volume>, <fpage>102575</fpage>. <pub-id pub-id-type="doi">10.1016/j.sbi.2023.102575</pub-id>
</citation>
</ref>
<ref id="B38">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Jin</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Wei</surname>
<given-names>Z.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Molecular simulation for food protein&#x2013;ligand interactions: a comprehensive review on principles, current applications, and emerging trends</article-title>. <source>Compr. Rev. Food Sci. Food Saf.</source> <volume>23</volume> (<issue>1</issue>), <fpage>132800</fpage>&#x2013;<lpage>e13329</lpage>. <pub-id pub-id-type="doi">10.1111/1541-4337.13280</pub-id>
</citation>
</ref>
<ref id="B39">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kang</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Zheng</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>B.</given-names>
</name>
</person-group> (<year>2020</year>). <article-title>Two dimensional nanomaterials-enabled smart light regulation technologies: recent advances and developments</article-title>. <source>Optik</source> <volume>220</volume>, <fpage>165191</fpage>. <pub-id pub-id-type="doi">10.1016/j.ijleo.2020.165191</pub-id>
</citation>
</ref>
<ref id="B40">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Kassa</surname>
<given-names>G.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Hartman</surname>
<given-names>T. W.</given-names>
</name>
<name>
<surname>Dhiman</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Gadhamshetty</surname>
<given-names>V.</given-names>
</name>
<name>
<surname>Gnimpieba</surname>
<given-names>E.</given-names>
</name>
</person-group> (<year>2023</year>). &#x201c;<article-title>Artificial intelligence based organic synthesis planning for material and bio-interface discovery</article-title>,&#x201d; in <source>Microbial stress response: mechanisms and data science</source> (<publisher-name>American Chemical Society</publisher-name>), <fpage>93</fpage>&#x2013;<lpage>111</lpage>.</citation>
</ref>
<ref id="B41">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Ullah</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Sajjad</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Jatoi</surname>
<given-names>W. B.</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<etal/>
</person-group> (<year>2019e</year>). <article-title>Controlled synthesis of ammonium manganese tri-fluoride nanoparticles with enhanced electrochemical performance</article-title>. <source>Mater. Res. Express</source> <volume>6</volume> (<issue>7</issue>), <fpage>075074</fpage>. <pub-id pub-id-type="doi">10.1088/2053-1591/ab18bb</pub-id>
</citation>
</ref>
<ref id="B42">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Khan Tareen</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Li</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>U.</given-names>
</name>
<name>
<surname>Nairan</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Yuan</surname>
<given-names>Y.</given-names>
</name>
<etal/>
</person-group> (<year>2018b</year>). <article-title>Facile synthesis of tin-doped mayenite electride composite as a non-noble metal durable electrocatalyst for oxygen reduction reaction (ORR)</article-title>. <source>Dalton Trans.</source> <volume>47</volume> (<issue>38</issue>), <fpage>13498</fpage>&#x2013;<lpage>13506</lpage>. <pub-id pub-id-type="doi">10.1039/c8dt02548g</pub-id>
</citation>
</ref>
<ref id="B43">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>M. F.</given-names>
</name>
<name>
<surname>Shi</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Ma</surname>
<given-names>C.</given-names>
</name>
<etal/>
</person-group> (<year>2020a</year>). <article-title>Synthesis, properties and novel electrocatalytic applications of the 2D-borophene Xenes</article-title>. <source>Prog. Solid State Chem.</source> <volume>59</volume>, <fpage>100283</fpage>. <pub-id pub-id-type="doi">10.1016/j.progsolidstchem.2020.100283</pub-id>
</citation>
</ref>
<ref id="B44">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>Q.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>S. A.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>Q. U.</given-names>
</name>
<etal/>
</person-group> (<year>2019b</year>). <article-title>Novel two-dimensional carbon&#x2013;chromium nitride-based composite as an electrocatalyst for oxygen reduction reaction</article-title>. <source>Front. Chem.</source> <volume>7</volume>, <fpage>738</fpage>. <pub-id pub-id-type="doi">10.3389/fchem.2019.00738</pub-id>
</citation>
</ref>
<ref id="B45">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>S. A.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>Q. U.</given-names>
</name>
<name>
<surname>Saeed</surname>
<given-names>M.</given-names>
</name>
<etal/>
</person-group> (<year>2019d</year>). <article-title>Fe-doped mayenite electride composite with 2D reduced graphene oxide: as a non-platinum based, highly durable electrocatalyst for Oxygen Reduction Reaction</article-title>. <source>Sci. Rep.</source> <volume>9</volume> (<issue>1</issue>), <fpage>19809</fpage>&#x2013;<lpage>19811</lpage>. <pub-id pub-id-type="doi">10.1038/s41598-019-55207-6</pub-id>
</citation>
</ref>
<ref id="B46">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Mahmood</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Ouyang</surname>
<given-names>Z.</given-names>
</name>
<etal/>
</person-group> (<year>2019c</year>). <article-title>Going green with batteries and supercapacitor: two dimensional materials and their nanocomposites based energy storage applications</article-title>. <source>Prog. Solid State Chem.</source> <volume>58</volume>, <fpage>100254</fpage>. <pub-id pub-id-type="doi">10.1016/j.progsolidstchem.2019.100254</pub-id>
</citation>
</ref>
<ref id="B47">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Sagar</surname>
<given-names>R. U. R.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>B.</given-names>
</name>
<name>
<surname>Huang</surname>
<given-names>W.</given-names>
</name>
<etal/>
</person-group> (<year>2020b</year>). <article-title>Recent progress, challenges, and prospects in two-dimensional photo-catalyst materials and environmental remediation</article-title>. <source>Nano-Micro Lett.</source> <volume>12</volume> (<issue>1</issue>), <fpage>167</fpage>&#x2013;<lpage>177</lpage>. <pub-id pub-id-type="doi">10.1007/s40820-020-00504-3</pub-id>
</citation>
</ref>
<ref id="B48">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Thebo</surname>
<given-names>K. H.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>U.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>R.</given-names>
</name>
<etal/>
</person-group> (<year>2019f</year>). <article-title>A comprehensive review on synthesis of pristine and doped inorganic room temperature stable mayenite electride,[Ca24Al28O64] 4&#x2b;(e&#x2212;) 4 and its applications as a catalyst</article-title>. <source>Prog. Solid State Chem.</source> <volume>54</volume>, <fpage>1</fpage>&#x2013;<lpage>19</lpage>. <pub-id pub-id-type="doi">10.1016/j.progsolidstchem.2018.12.001</pub-id>
</citation>
</ref>
<ref id="B49">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Mahmood</surname>
<given-names>A.</given-names>
</name>
<etal/>
</person-group> (<year>2020c</year>). <article-title>Recent developments in emerging two-dimensional materials and their applications</article-title>. <source>J. Mater. Chem. C</source> <volume>8</volume> (<issue>2</issue>), <fpage>387</fpage>&#x2013;<lpage>440</lpage>. <pub-id pub-id-type="doi">10.1039/c9tc04187g</pub-id>
</citation>
</ref>
<ref id="B50">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>S. A.</given-names>
</name>
<etal/>
</person-group> (<year>2020d</year>). <article-title>Facile synthesis of mayenite electride nanoparticles encapsulated in graphitic shells like carbon nano onions: non-noble-metal electrocatalysts for oxygen reduction reaction (ORR)</article-title>. <source>Front. Chem.</source> <volume>7</volume>, <fpage>934</fpage>. <pub-id pub-id-type="doi">10.3389/fchem.2019.00934</pub-id>
</citation>
</ref>
<ref id="B51">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Ouyang</surname>
<given-names>Z.</given-names>
</name>
<etal/>
</person-group> (<year>2019a</year>). <article-title>Recent advances in two-dimensional materials and their nanocomposites in sustainable energy conversion applications</article-title>. <source>Nanoscale</source> <volume>11</volume> (<issue>45</issue>), <fpage>21622</fpage>&#x2013;<lpage>21678</lpage>. <pub-id pub-id-type="doi">10.1039/c9nr05919a</pub-id>
</citation>
</ref>
<ref id="B52">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Elshahat</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Muhammad</surname>
<given-names>N.</given-names>
</name>
<name>
<surname>Li</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Aboodd</surname>
<given-names>I.</given-names>
</name>
<etal/>
</person-group> (<year>2018d</year>). <article-title>Facile metal-free reduction-based synthesis of pristine and cation-doped conductive mayenite</article-title>. <source>Rsc Adv.</source> <volume>8</volume> (<issue>43</issue>), <fpage>24276</fpage>&#x2013;<lpage>24285</lpage>. <pub-id pub-id-type="doi">10.1039/c8ra02790k</pub-id>
</citation>
</ref>
<ref id="B53">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Elshahat</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Yadav</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>U.</given-names>
</name>
<name>
<surname>Yang</surname>
<given-names>M.</given-names>
</name>
<etal/>
</person-group> (<year>2018c</year>). <article-title>Facile synthesis of a cationic-doped [Ca24Al28O64] 4&#x2b;(4e&#x2212;) composite via a rapid citrate sol-gel method</article-title>. <source>Dalton Trans.</source> <volume>47</volume> (<issue>11</issue>), <fpage>3819</fpage>&#x2013;<lpage>3830</lpage>. <pub-id pub-id-type="doi">10.1039/c7dt04543c</pub-id>
</citation>
</ref>
<ref id="B54">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Iqbal</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Mahmood</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Shi</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Yin</surname>
<given-names>J.</given-names>
</name>
<etal/>
</person-group> (<year>2021b</year>). <article-title>Recent development in graphdiyne and its derivative materials for novel biomedical applications</article-title>. <source>J. Mater. Chem. B</source> <volume>9</volume>, <fpage>9461</fpage>&#x2013;<lpage>9484</lpage>. <pub-id pub-id-type="doi">10.1039/d1tb01794b</pub-id>
</citation>
</ref>
<ref id="B55">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Iqbal</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Shi</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Guo</surname>
<given-names>Z.</given-names>
</name>
</person-group> (<year>2021d</year>). <article-title>Novel emerging graphdiyne based two dimensional materials: synthesis, properties and renewable energy applications</article-title>. <source>Nano Today</source> <volume>39</volume>, <fpage>101207</fpage>. <pub-id pub-id-type="doi">10.1016/j.nantod.2021.101207</pub-id>
</citation>
</ref>
<ref id="B56">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Iqbal</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Ma</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Shi</surname>
<given-names>Z.</given-names>
</name>
<etal/>
</person-group> (<year>2021e</year>). <article-title>Navigating recent advances in monoelemental materials (Xenes)-fundamental to biomedical applications</article-title>. <source>Prog. Solid State Chem.</source> <volume>63</volume>, <fpage>100326</fpage>. <pub-id pub-id-type="doi">10.1016/j.progsolidstchem.2021.100326</pub-id>
</citation>
</ref>
<ref id="B57">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Iqbal</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Ye</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Xie</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Mahmood</surname>
<given-names>A.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Recent progress in emerging novel MXenes based materials and their fascinating sensing applications</article-title>. <source>Small</source> <volume>19</volume>, <fpage>2206147</fpage>. <pub-id pub-id-type="doi">10.1002/smll.202206147</pub-id>
</citation>
</ref>
<ref id="B58">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>Q. U.</given-names>
</name>
<name>
<surname>Iqbal</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Guo</surname>
<given-names>Z.</given-names>
</name>
</person-group> (<year>2021c</year>). <article-title>Novel synthesis, properties and applications of emerging group VA two-dimensional monoelemental materials (2D-Xenes)</article-title>. <source>Mater. Chem. Front.</source> <volume>5</volume> (<issue>17</issue>), <fpage>6333</fpage>&#x2013;<lpage>6391</lpage>. <pub-id pub-id-type="doi">10.1039/d1qm00629k</pub-id>
</citation>
</ref>
<ref id="B59">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>tareen</surname>
<given-names>A. k.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>U.</given-names>
</name>
<name>
<surname>Nairan</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Elshahat</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Muhammad</surname>
<given-names>N.</given-names>
</name>
<etal/>
</person-group> (<year>2018a</year>). <article-title>Single step synthesis of highly conductive room-temperature stable cation-substituted mayenite electride target and thin film</article-title>. <source>Sci. Rep.</source> <volume>9</volume>, <fpage>4967</fpage>. <comment>SREP-18-33491B</comment>. <pub-id pub-id-type="doi">10.1038/s41598-019-41512-7</pub-id>
</citation>
</ref>
<ref id="B60">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Ma</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Mahmood</surname>
<given-names>N.</given-names>
</name>
<etal/>
</person-group> (<year>2021a</year>). <article-title>Sensing applications of atomically thin group iv carbon siblings xenes: progress, challenges, and prospects</article-title>. <source>Adv. Funct. Mater.</source> <volume>31</volume> (<issue>3</issue>), <fpage>2005957</fpage>. <pub-id pub-id-type="doi">10.1002/adfm.202005957</pub-id>
</citation>
</ref>
<ref id="B61">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>P. M.</given-names>
</name>
<name>
<surname>Jillella</surname>
<given-names>G. K.</given-names>
</name>
<name>
<surname>Roy</surname>
<given-names>K.</given-names>
</name>
</person-group> (<year>2024</year>). &#x201c;<article-title>Recent advancements in QSAR and machine learning approaches for risk assessment of organic chemicals</article-title>,&#x201d; in <source>QSAR in safety evaluation and risk assessment</source> (<publisher-name>Academic Press</publisher-name>), <fpage>167</fpage>&#x2013;<lpage>185</lpage>.</citation>
</ref>
<ref id="B62">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khatoon</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Guo</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Attique</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Treen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Haq</surname>
<given-names>M. U.</given-names>
</name>
<etal/>
</person-group> (<year>2020</year>). <article-title>Facile synthesis of &#x3b1;-Fe2O3/Nb2O5 heterostructure for advanced Li-Ion batteries</article-title>. <source>J. Alloys Compd.</source> <volume>837</volume>, <fpage>155294</fpage>. <pub-id pub-id-type="doi">10.1016/j.jallcom.2020.155294</pub-id>
</citation>
</ref>
<ref id="B63">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kilonzi</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Mutagonda</surname>
<given-names>R. F.</given-names>
</name>
<name>
<surname>Mwakawanga</surname>
<given-names>D. L.</given-names>
</name>
<name>
<surname>Mlyuka</surname>
<given-names>H. J.</given-names>
</name>
<name>
<surname>Mikomangwa</surname>
<given-names>W. P.</given-names>
</name>
<name>
<surname>Kibanga</surname>
<given-names>W. A.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Establishment of clinical pharmacy services: evidence-based information from stakeholders</article-title>. <source>Hum. Resour. Health</source> <volume>22</volume> (<issue>1</issue>), <fpage>6</fpage>&#x2013;<lpage>4</lpage>. <pub-id pub-id-type="doi">10.1186/s12960-023-00887-5</pub-id>
</citation>
</ref>
<ref id="B64">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Klauschen</surname>
<given-names>F.</given-names>
</name>
<name>
<surname>Dippel</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Keyl</surname>
<given-names>P.</given-names>
</name>
<name>
<surname>Jurmeister</surname>
<given-names>P.</given-names>
</name>
<name>
<surname>Bockmayr</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Mock</surname>
<given-names>A.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Toward explainable artificial intelligence for precision pathology</article-title>. <source>Annu. Rev. Pathology Mech. Dis.</source> <volume>19</volume>, <fpage>541</fpage>&#x2013;<lpage>570</lpage>. <pub-id pub-id-type="doi">10.1146/annurev-pathmechdis-051222-113147</pub-id>
</citation>
</ref>
<ref id="B65">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kotkondawar</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Sutar</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Kiwelekar</surname>
<given-names>A.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Applications of deep learning in bioinformatics and drug discovery</article-title>. <source>Authorea Prepr</source>. <pub-id pub-id-type="doi">10.22541/au.170433004.48304398/v1</pub-id>
</citation>
</ref>
<ref id="B66">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kumar</surname>
<given-names>N.</given-names>
</name>
<name>
<surname>Acharya</surname>
<given-names>V.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Advances in machine intelligence&#x2010;driven virtual screening approaches for big&#x2010;data</article-title>. <source>Med. Res. Rev.</source> <volume>44</volume>, <fpage>939</fpage>&#x2013;<lpage>974</lpage>. <pub-id pub-id-type="doi">10.1002/med.21995</pub-id>
</citation>
</ref>
<ref id="B67">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Li</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Long</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Hussain</surname>
<given-names>I.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>M. F.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Highly efficient, remarkable sensor activity and energy storage properties of MXenes and borophene nanomaterials</article-title>. <source>Prog. Solid State Chem.</source> <volume>70</volume>, <fpage>100392</fpage>. <pub-id pub-id-type="doi">10.1016/j.progsolidstchem.2023.100392</pub-id>
</citation>
</ref>
<ref id="B68">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>L&#xf3;pez</surname>
<given-names>C.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Artificial intelligence and advanced materials</article-title>. <source>Adv. Mater.</source> <volume>35</volume> (<issue>23</issue>), <fpage>2208683</fpage>. <pub-id pub-id-type="doi">10.1002/adma.202208683</pub-id>
</citation>
</ref>
<ref id="B69">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Mittal</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Ahuja</surname>
<given-names>G.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Advancing chemical carcinogenicity prediction modeling: opportunities and challenges</article-title>. <source>Trends Pharmacol. Sci.</source> <volume>44</volume>, <fpage>400</fpage>&#x2013;<lpage>410</lpage>. <pub-id pub-id-type="doi">10.1016/j.tips.2023.04.002</pub-id>
</citation>
</ref>
<ref id="B70">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Nasnodkar</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Cinar</surname>
<given-names>B.</given-names>
</name>
<name>
<surname>Ness</surname>
<given-names>S.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Artificial intelligence in toxicology and pharmacology</article-title>. <source>J. Eng. Res. Rep.</source> <volume>25</volume> (<issue>7</issue>), <fpage>192</fpage>&#x2013;<lpage>206</lpage>. <pub-id pub-id-type="doi">10.9734/jerr/2023/v25i7952</pub-id>
</citation>
</ref>
<ref id="B71">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Nebreda</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Shpakivska-Bilan</surname>
<given-names>D.</given-names>
</name>
<name>
<surname>Camara</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Susi</surname>
<given-names>G.</given-names>
</name>
</person-group> (<year>2024</year>). &#x201c;<article-title>The social machine: artificial intelligence (AI) approaches to theory of mind</article-title>,&#x201d; in <source>The theory of mind under scrutiny: psychopathology, neuroscience, philosophy of mind and artificial intelligence</source> (<publisher-loc>Cham</publisher-loc>: <publisher-name>Springer Nature Switzerland</publisher-name>), <fpage>681</fpage>&#x2013;<lpage>722</lpage>.</citation>
</ref>
<ref id="B72">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Nguyen</surname>
<given-names>N. Q.</given-names>
</name>
<name>
<surname>Park</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Gim</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Kang</surname>
<given-names>J.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>MulinforCPI: enhancing precision of compound&#x2013;protein interaction prediction through novel perspectives on multi-level information integration</article-title>. <source>Briefings Bioinforma.</source> <volume>25</volume> (<issue>1</issue>), <fpage>bbad484</fpage>. <pub-id pub-id-type="doi">10.1093/bib/bbad484</pub-id>
</citation>
</ref>
<ref id="B73">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Noor</surname>
<given-names>F.</given-names>
</name>
<name>
<surname>Asif</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Ashfaq</surname>
<given-names>U. A.</given-names>
</name>
<name>
<surname>Qasim</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Tahir ul Qamar</surname>
<given-names>M.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Machine learning for synergistic network pharmacology: a comprehensive overview</article-title>. <source>Briefings Bioinforma.</source> <volume>24</volume>, <fpage>bbad120</fpage>. <pub-id pub-id-type="doi">10.1093/bib/bbad120</pub-id>
</citation>
</ref>
<ref id="B74">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Oliveira</surname>
<given-names>T. A. D.</given-names>
</name>
<name>
<surname>Silva</surname>
<given-names>M. P. D.</given-names>
</name>
<name>
<surname>Maia</surname>
<given-names>E. H. B.</given-names>
</name>
<name>
<surname>Silva</surname>
<given-names>A. M. D.</given-names>
</name>
<name>
<surname>Taranto</surname>
<given-names>A. G.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Virtual screening algorithms in drug discovery: a review focused on machine and deep learning methods</article-title>. <source>Drugs Drug Candidates</source> <volume>2</volume> (<issue>2</issue>), <fpage>311</fpage>&#x2013;<lpage>334</lpage>. <pub-id pub-id-type="doi">10.3390/ddc2020017</pub-id>
</citation>
</ref>
<ref id="B75">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Pham</surname>
<given-names>P.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Gao</surname>
<given-names>W.</given-names>
</name>
<name>
<surname>Zhu</surname>
<given-names>X.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Determinants and performance outcomes of artificial intelligence adoption: evidence from US Hospitals</article-title>. <source>J. Bus. Res.</source> <volume>172</volume>, <fpage>114402</fpage>. <pub-id pub-id-type="doi">10.1016/j.jbusres.2023.114402</pub-id>
</citation>
</ref>
<ref id="B76">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Philippe</surname>
<given-names>G. J. B.</given-names>
</name>
<name>
<surname>Huang</surname>
<given-names>Y. H.</given-names>
</name>
<name>
<surname>Mittermeier</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Brown</surname>
<given-names>C. J.</given-names>
</name>
<name>
<surname>Kaas</surname>
<given-names>Q.</given-names>
</name>
<name>
<surname>Ramlan</surname>
<given-names>S. R.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Delivery to, and reactivation of, the p53 pathway in cancer cells using a grafted cyclotide conjugated with a cell-penetrating peptide</article-title>. <source>J. Med. Chem.</source> <volume>67</volume>, <fpage>1197</fpage>&#x2013;<lpage>1208</lpage>. <pub-id pub-id-type="doi">10.1021/acs.jmedchem.3c01682</pub-id>
</citation>
</ref>
<ref id="B77">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Prasad</surname>
<given-names>K. D. V.</given-names>
</name>
<name>
<surname>Kalavakolanu</surname>
<given-names>S.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>The study of cognitive psychology in conjunction with artificial intelligence</article-title>. <source>Conhecimento Divers.</source> <volume>15</volume> (<issue>36</issue>), <fpage>270</fpage>&#x2013;<lpage>286</lpage>. <pub-id pub-id-type="doi">10.18316/rcd.v15i36.10788</pub-id>
</citation>
</ref>
<ref id="B78">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Prem</surname>
<given-names>E.</given-names>
</name>
</person-group> (<year>2023</year>) <source>From ethical AI frameworks to tools: a review of approaches. <italic>AI and Ethics</italic>
</source>, <fpage>1</fpage>&#x2013;<lpage>18</lpage>.</citation>
</ref>
<ref id="B79">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Pun</surname>
<given-names>F. W.</given-names>
</name>
<name>
<surname>Ozerov</surname>
<given-names>I. V.</given-names>
</name>
<name>
<surname>Zhavoronkov</surname>
<given-names>A.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>AI-powered therapeutic target discovery</article-title>. <source>Trends Pharmacol. Sci.</source> <volume>44</volume>, <fpage>561</fpage>&#x2013;<lpage>572</lpage>. <pub-id pub-id-type="doi">10.1016/j.tips.2023.06.010</pub-id>
</citation>
</ref>
<ref id="B80">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ramos</surname>
<given-names>P. I. P.</given-names>
</name>
<name>
<surname>Marcilio</surname>
<given-names>I.</given-names>
</name>
<name>
<surname>Bento</surname>
<given-names>A. I.</given-names>
</name>
<name>
<surname>Penna</surname>
<given-names>G. O.</given-names>
</name>
<name>
<surname>de Oliveira</surname>
<given-names>J. F.</given-names>
</name>
<name>
<surname>Khouri</surname>
<given-names>R.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Combining digital and molecular approaches using health and alternate data sources in a next-generation surveillance system for anticipating outbreaks of pandemic potential</article-title>. <source>JMIR Public Health Surveillance</source> <volume>10</volume>, <fpage>e47673</fpage>. <pub-id pub-id-type="doi">10.2196/47673</pub-id>
</citation>
</ref>
<ref id="B81">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Rasool</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Husnain</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Saeed</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Gill</surname>
<given-names>A. Y.</given-names>
</name>
<name>
<surname>Hussain</surname>
<given-names>H. K.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Harnessing predictive power: exploring the crucial role of machine learning in early disease detection</article-title>. <source>JURIHUM J. Inov. Dan. Hum.</source> <volume>1</volume> (<issue>2</issue>), <fpage>302</fpage>&#x2013;<lpage>315</lpage>.</citation>
</ref>
<ref id="B82">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ratten</surname>
<given-names>V.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Artificial intelligence, digital trends and globalization: future research trends</article-title>. <source>FIIB Bus. Rev.</source>, <fpage>23197145231222774</fpage>. <pub-id pub-id-type="doi">10.1177/23197145231222774</pub-id>
</citation>
</ref>
<ref id="B83">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Rehman</surname>
<given-names>A. U.</given-names>
</name>
<name>
<surname>Khurshid</surname>
<given-names>B.</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Rasheed</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Wadood</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Ng</surname>
<given-names>H. L.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Computational approaches for the design of modulators targeting protein-protein interactions</article-title>. <source>Expert Opin. drug Discov.</source> <volume>18</volume> (<issue>3</issue>), <fpage>315</fpage>&#x2013;<lpage>333</lpage>. <pub-id pub-id-type="doi">10.1080/17460441.2023.2171396</pub-id>
</citation>
</ref>
<ref id="B84">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Sagar</surname>
<given-names>R. U. R.</given-names>
</name>
<name>
<surname>Zaiping</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Iqbal</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Rehman</surname>
<given-names>S. U.</given-names>
</name>
<name>
<surname>Ashraf</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>C.</given-names>
</name>
<etal/>
</person-group> (<year>2021</year>) <source>Extremely large, linear, and controllable positive magnetoresistance in neodymium-doped graphene foam for magnetic sensors</source>, <fpage>100460</fpage>.</citation>
</ref>
<ref id="B85">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sagar</surname>
<given-names>R. U. R.</given-names>
</name>
<name>
<surname>Rahman</surname>
<given-names>M. M.</given-names>
</name>
<name>
<surname>Cai</surname>
<given-names>Q.</given-names>
</name>
<name>
<surname>Liang</surname>
<given-names>T.</given-names>
</name>
<name>
<surname>Chen</surname>
<given-names>Y. I. J. J.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>A comparative study on morphology dependent performance of neodymium&#x2013;graphene as an anode material in lithium-ion batteries</article-title>. <source>J. Energy Storage</source> <volume>77</volume>, <fpage>109854</fpage>. <pub-id pub-id-type="doi">10.1016/j.est.2023.109854</pub-id>
</citation>
</ref>
<ref id="B86">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sanchez</surname>
<given-names>D.</given-names>
</name>
<name>
<surname>Slovacek</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>R.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Shaping the future of men&#x27;s sexual health: how artificial intelligence can assist in the management and treatment of erectile dysfunction</article-title>. <source>UroPrecision</source>. <pub-id pub-id-type="doi">10.1002/uro2.31</pub-id>
</citation>
</ref>
<ref id="B87">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sankaranarayanan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Jensen</surname>
<given-names>K. F.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Computer-assisted multistep chemoenzymatic retrosynthesis using a chemical synthesis planner</article-title>. <source>Chem. Sci.</source> <volume>14</volume> (<issue>23</issue>), <fpage>6467</fpage>&#x2013;<lpage>6475</lpage>. <pub-id pub-id-type="doi">10.1039/d3sc01355c</pub-id>
</citation>
</ref>
<ref id="B88">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sarkar</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Das</surname>
<given-names>B.</given-names>
</name>
<name>
<surname>Rawat</surname>
<given-names>V. S.</given-names>
</name>
<name>
<surname>Wahlang</surname>
<given-names>J. B.</given-names>
</name>
<name>
<surname>Nongpiur</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Tiewsoh</surname>
<given-names>I.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Artificial intelligence and machine learning technology driven modern drug discovery and development</article-title>. <source>Int. J. Mol. Sci.</source> <volume>24</volume> (<issue>3</issue>), <fpage>2026</fpage>. <pub-id pub-id-type="doi">10.3390/ijms24032026</pub-id>
</citation>
</ref>
<ref id="B89">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Shaheen</surname>
<given-names>I.</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>I.</given-names>
</name>
<name>
<surname>Bibi</surname>
<given-names>F.</given-names>
</name>
<name>
<surname>Hanan</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Ahmad</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Hussain</surname>
<given-names>I.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Integrating 1D/2D nanostructure based on Ni&#x2013;Co-oxalate for energy storage applications</article-title>. <source>Ceram. Int.</source> <volume>50</volume>, <fpage>10789</fpage>&#x2013;<lpage>10796</lpage>. <pub-id pub-id-type="doi">10.1016/j.ceramint.2023.12.394</pub-id>
</citation>
</ref>
<ref id="B90">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Shi</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Cao</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Xu</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H.</given-names>
</name>
</person-group> (<year>2022</year>). <article-title>Two-dimensional selenium and its composites for device applications</article-title>. <source>Nano Res.</source> <volume>15</volume> (<issue>1</issue>), <fpage>104</fpage>&#x2013;<lpage>122</lpage>. <pub-id pub-id-type="doi">10.1007/s12274-021-3493-x</pub-id>
</citation>
</ref>
<ref id="B91">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Shiota</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Suma</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Ogawa</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Yamaguchi</surname>
<given-names>T.</given-names>
</name>
<name>
<surname>Iida</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Hata</surname>
<given-names>T.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>AQDnet: deep neural network for protein&#x2013;ligand docking simulation</article-title>. <source>ACS Omega</source> <volume>8</volume>, <fpage>23925</fpage>&#x2013;<lpage>23935</lpage>. <pub-id pub-id-type="doi">10.1021/acsomega.3c02411</pub-id>
</citation>
</ref>
<ref id="B92">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sim</surname>
<given-names>J. A.</given-names>
</name>
<name>
<surname>Huang</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Horan</surname>
<given-names>M. R.</given-names>
</name>
<name>
<surname>Stewart</surname>
<given-names>C. M.</given-names>
</name>
<name>
<surname>Robison</surname>
<given-names>L. L.</given-names>
</name>
<name>
<surname>Hudson</surname>
<given-names>M. M.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: a systematic review</article-title>. <source>Artif. Intell. Med.</source> <volume>146</volume>, <fpage>102701</fpage>. <pub-id pub-id-type="doi">10.1016/j.artmed.2023.102701</pub-id>
</citation>
</ref>
<ref id="B93">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Singh</surname>
<given-names>V.</given-names>
</name>
<name>
<surname>Khatun</surname>
<given-names>A.</given-names>
</name>
</person-group> (<year>2024</year>). &#x201c;<article-title>Application of artificial intelligence in human resource management: a conceptual framework</article-title>,&#x201d; in <source>The role of HR in the transforming workplace</source> (<publisher-loc>Taylor &#x26; Francis Group, England </publisher-loc>: <publisher-name>Productivity Press</publisher-name>), <fpage>32</fpage>&#x2013;<lpage>49</lpage>.</citation>
</ref>
<ref id="B94">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sinha</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Ghosh</surname>
<given-names>N.</given-names>
</name>
<name>
<surname>Sil</surname>
<given-names>P. C.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>A review on the recent applications of deep learning in predictive drug toxicological studies</article-title>. <source>Chem. Res. Toxicol.</source> <volume>36</volume> (<issue>8</issue>), <fpage>1174</fpage>&#x2013;<lpage>1205</lpage>. <pub-id pub-id-type="doi">10.1021/acs.chemrestox.2c00375</pub-id>
</citation>
</ref>
<ref id="B95">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Stevenson</surname>
<given-names>G. A.</given-names>
</name>
<name>
<surname>Kirshner</surname>
<given-names>D.</given-names>
</name>
<name>
<surname>Bennion</surname>
<given-names>B. J.</given-names>
</name>
<name>
<surname>Yang</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Zemla</surname>
<given-names>A.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>Clustering protein binding pockets and identifying potential drug interactions: a novel ligand-based featurization method</article-title>. <source>J. Chem. Inf. Model.</source> <volume>63</volume> (<issue>21</issue>), <fpage>6655</fpage>&#x2013;<lpage>6666</lpage>. <pub-id pub-id-type="doi">10.1021/acs.jcim.3c00722</pub-id>
</citation>
</ref>
<ref id="B96">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tang</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Sagar</surname>
<given-names>R. U. R.</given-names>
</name>
<name>
<surname>Luo</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<etal/>
</person-group> (<year>2021</year>). <article-title>Graphene foam&#x2013;polymer based electronic skin for flexible tactile sensor</article-title>. <source>Sensors Actuators A Phys.</source> <volume>327</volume>, <fpage>112697</fpage>. <pub-id pub-id-type="doi">10.1016/j.sna.2021.112697</pub-id>
</citation>
</ref>
<ref id="B97">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H.</given-names>
</name>
</person-group> (<year>2021b</year>). <article-title>Confinement in two-dimensional materials: major advances and challenges in the emerging renewable energy conversion and other applications</article-title>. <source>Prog. Solid State Chem.</source> <volume>61</volume>, <fpage>100294</fpage>. <pub-id pub-id-type="doi">10.1016/j.progsolidstchem.2020.100294</pub-id>
</citation>
</ref>
<ref id="B98">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Aslam</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>X.</given-names>
</name>
</person-group> (<year>2021a</year>). <article-title>Recent progress, challenges, and prospects in emerging group-VIA Xenes: synthesis, properties and novel applications</article-title>. <source>Nanoscale</source> <volume>13</volume> (<issue>2</issue>), <fpage>510</fpage>&#x2013;<lpage>552</lpage>. <pub-id pub-id-type="doi">10.1039/d0nr07444f</pub-id>
</citation>
</ref>
<ref id="B99">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Iqbal</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Long</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Mahmood</surname>
<given-names>A.</given-names>
</name>
<etal/>
</person-group> (<year>2022b</year>). <article-title>Recent advance in two-dimensional MXenes: new horizons in flexible batteries and supercapacitors technologies</article-title>. <source>Energy Storage Mater.</source> <volume>53</volume>, <fpage>783</fpage>&#x2013;<lpage>826</lpage>. <pub-id pub-id-type="doi">10.1016/j.ensm.2022.09.030</pub-id>
</citation>
</ref>
<ref id="B100">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Rehman</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Iqbal</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Yu</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Zhou</surname>
<given-names>Z.</given-names>
</name>
<etal/>
</person-group> (<year>2022a</year>). <article-title>Recent development in emerging phosphorene based novel materials: progress, challenges, prospects and their fascinating sensing applications</article-title>. <source>Prog. Solid State Chem.</source> <volume>65</volume>, <fpage>100336</fpage>. <pub-id pub-id-type="doi">10.1016/j.progsolidstchem.2021.100336</pub-id>
</citation>
</ref>
<ref id="B101">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Thangavel</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Sabatini</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Gardi</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Ranasinghe</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Hilton</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Servidia</surname>
<given-names>P.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Artificial intelligence for trusted autonomous satellite operations</article-title>. <source>Prog. Aerosp. Sci.</source> <volume>144</volume>, <fpage>100960</fpage>. <pub-id pub-id-type="doi">10.1016/j.paerosci.2023.100960</pub-id>
</citation>
</ref>
<ref id="B102">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Thenuwara</surname>
<given-names>G.</given-names>
</name>
<name>
<surname>Curtin</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Tian</surname>
<given-names>F.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Advances in diagnostic tools and therapeutic approaches for gliomas: a comprehensive review</article-title>. <source>Sensors</source> <volume>23</volume> (<issue>24</issue>), <fpage>9842</fpage>. <pub-id pub-id-type="doi">10.3390/s23249842</pub-id>
</citation>
</ref>
<ref id="B103">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tran</surname>
<given-names>T. T. V.</given-names>
</name>
<name>
<surname>Surya Wibowo</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Tayara</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Chong</surname>
<given-names>K. T.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives</article-title>. <source>J. Chem. Inf. Model.</source> <volume>63</volume> (<issue>9</issue>), <fpage>2628</fpage>&#x2013;<lpage>2643</lpage>. <pub-id pub-id-type="doi">10.1021/acs.jcim.3c00200</pub-id>
</citation>
</ref>
<ref id="B104">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tripathi</surname>
<given-names>S. A.</given-names>
</name>
<name>
<surname>Rohokale</surname>
<given-names>M. S.</given-names>
</name>
<name>
<surname>Kumar</surname>
<given-names>S.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Advances in the treatment of cognitive diseases using IOT-based wearable devices</article-title>. <source>Cognitive Predict. Maintenance Tools Brain Dis.</source>, <fpage>138</fpage>&#x2013;<lpage>157</lpage>. <pub-id pub-id-type="doi">10.1201/9781003245346-9</pub-id>
</citation>
</ref>
<ref id="B105">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tropsha</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Isayev</surname>
<given-names>O.</given-names>
</name>
<name>
<surname>Varnek</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Schneider</surname>
<given-names>G.</given-names>
</name>
<name>
<surname>Cherkasov</surname>
<given-names>A.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR</article-title>. <source>Nat. Rev. Drug Discov.</source> <volume>23</volume>, <fpage>141</fpage>&#x2013;<lpage>155</lpage>. <pub-id pub-id-type="doi">10.1038/s41573-023-00832-0</pub-id>
</citation>
</ref>
<ref id="B106">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Turon</surname>
<given-names>G.</given-names>
</name>
<name>
<surname>Hlozek</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Woodland</surname>
<given-names>J. G.</given-names>
</name>
<name>
<surname>Kumar</surname>
<given-names>A.</given-names>
</name>
<name>
<surname>Chibale</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Duran-Frigola</surname>
<given-names>M.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa</article-title>. <source>Nat. Commun.</source> <volume>14</volume> (<issue>1</issue>), <fpage>5736</fpage>. <pub-id pub-id-type="doi">10.1038/s41467-023-41512-2</pub-id>
</citation>
</ref>
<ref id="B107">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Vora</surname>
<given-names>L. K.</given-names>
</name>
<name>
<surname>Gholap</surname>
<given-names>A. D.</given-names>
</name>
<name>
<surname>Jetha</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Thakur</surname>
<given-names>R. R. S.</given-names>
</name>
<name>
<surname>Solanki</surname>
<given-names>H. K.</given-names>
</name>
<name>
<surname>Chavda</surname>
<given-names>V. P.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Artificial intelligence in pharmaceutical technology and drug delivery design</article-title>. <source>Pharmaceutics</source> <volume>15</volume> (<issue>7</issue>), <fpage>1916</fpage>. <pub-id pub-id-type="doi">10.3390/pharmaceutics15071916</pub-id>
</citation>
</ref>
<ref id="B108">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Wang</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Dai</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Jiang</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Tong</surname>
<given-names>G.</given-names>
</name>
<name>
<surname>Xiong</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<etal/>
</person-group> (<year>2021</year>). <article-title>Advanced devices for tumor diagnosis and therapy</article-title>. <source>Small</source> <volume>17</volume>, <fpage>2100003</fpage>. <pub-id pub-id-type="doi">10.1002/smll.202100003</pub-id>
</citation>
</ref>
<ref id="B109">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Wardat</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Tashtoush</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>AlAli</surname>
<given-names>R.</given-names>
</name>
<name>
<surname>Saleh</surname>
<given-names>S.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>Artificial intelligence in education: mathematics teachers&#x2019; perspectives, practices and challenges</article-title>. <source>Iraqi J. Comput. Sci. Math.</source> <volume>5</volume> (<issue>1</issue>), <fpage>60</fpage>&#x2013;<lpage>77</lpage>. <pub-id pub-id-type="doi">10.52866/ijcsm.2024.05.01.004</pub-id>
</citation>
</ref>
<ref id="B110">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Wilde</surname>
<given-names>A. M.</given-names>
</name>
<name>
<surname>Song</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Allen</surname>
<given-names>W. P.</given-names>
</name>
<name>
<surname>Junkins</surname>
<given-names>A. D.</given-names>
</name>
<name>
<surname>Frazier</surname>
<given-names>J. M.</given-names>
</name>
<name>
<surname>Moore</surname>
<given-names>S. E.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Implementation of a pharmacy-driven rapid bacteremia response program</article-title>. <source>Am. J. Health-System Pharm.</source> <volume>81</volume> (<issue>2</issue>), <fpage>74</fpage>&#x2013;<lpage>82</lpage>. <pub-id pub-id-type="doi">10.1093/ajhp/zxad211</pub-id>
</citation>
</ref>
<ref id="B111">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Xie</surname>
<given-names>W.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Xie</surname>
<given-names>Q.</given-names>
</name>
<name>
<surname>Gong</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Xu</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Lai</surname>
<given-names>L.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>) <source>Accelerating discovery of novel and bioactive ligands with pharmacophore-informed generative models</source>. <comment>arXiv preprint arXiv:2401.01059</comment>.</citation>
</ref>
<ref id="B112">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yaiprasert</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Hidayanto</surname>
<given-names>A. N.</given-names>
</name>
</person-group> (<year>2024</year>). <article-title>AI-powered ensemble machine learning to optimize cost strategies in logistics business</article-title>. <source>Int. J. Inf. Manag. Data Insights</source> <volume>4</volume> (<issue>1</issue>), <fpage>100209</fpage>. <pub-id pub-id-type="doi">10.1016/j.jjimei.2023.100209</pub-id>
</citation>
</ref>
<ref id="B113">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yan</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Zhao</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Yao</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Feng</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Liang</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Han</surname>
<given-names>W.</given-names>
</name>
<etal/>
</person-group> (<year>2023</year>). <article-title>RPBP: deep retrosynthesis reaction prediction based on byproducts</article-title>. <source>J. Chem. Inf. Model.</source> <volume>63</volume> (<issue>19</issue>), <fpage>5956</fpage>&#x2013;<lpage>5970</lpage>. <pub-id pub-id-type="doi">10.1021/acs.jcim.3c00274</pub-id>
</citation>
</ref>
<ref id="B114">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yang</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Kar</surname>
<given-names>S.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity</article-title>. <source>Artif. Intell. Chem.</source> <volume>1</volume>, <fpage>100011</fpage>. <pub-id pub-id-type="doi">10.1016/j.aichem.2023.100011</pub-id>
</citation>
</ref>
<ref id="B115">
<citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname>Yao</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>Z.</given-names>
</name>
<name>
<surname>Guo</surname>
<given-names>W.</given-names>
</name>
<name>
<surname>Xiang</surname>
<given-names>S.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>W.</given-names>
</name>
<name>
<surname>Ke</surname>
<given-names>G.</given-names>
</name>
</person-group> (<year>2023</year>) <source>Node-aligned graph-to-graph generation for retrosynthesis prediction</source>. <comment>arXiv preprint arXiv:2309.15798</comment>.</citation>
</ref>
<ref id="B116">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yin</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Sharma</surname>
<given-names>B.</given-names>
</name>
<name>
<surname>Hu</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>F.</given-names>
</name>
<name>
<surname>Kaur</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Cohen</surname>
<given-names>G.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Predicting the climate impact of healthcare facilities using gradient boosting machines</article-title>. <source>Clean. Environ. Syst.</source> <volume>12</volume>, <fpage>100155</fpage>. <pub-id pub-id-type="doi">10.1016/j.cesys.2023.100155</pub-id>
</citation>
</ref>
<ref id="B117">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zeng</surname>
<given-names>T.</given-names>
</name>
<name>
<surname>Jin</surname>
<given-names>B.</given-names>
</name>
<name>
<surname>Glade</surname>
<given-names>T.</given-names>
</name>
<name>
<surname>Xie</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Li</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Zhu</surname>
<given-names>Y.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: a critical inquiry</article-title>. <source>Catena</source> <volume>236</volume>, <fpage>107732</fpage>. <pub-id pub-id-type="doi">10.1016/j.catena.2023.107732</pub-id>
</citation>
</ref>
<ref id="B118">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zhang</surname>
<given-names>L.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Zou</surname>
<given-names>J.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Li</surname>
<given-names>Y.</given-names>
</name>
</person-group> (<year>2019</year>). <article-title>Recent advances in emerging 2D material-based gas sensors: potential in disease diagnosis</article-title>. <source>Adv. Mater. Interfaces</source> <volume>6</volume> (<issue>22</issue>), <fpage>1901329</fpage>. <pub-id pub-id-type="doi">10.1002/admi.201901329</pub-id>
</citation>
</ref>
<ref id="B119">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zhang</surname>
<given-names>X.</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>K.</given-names>
</name>
<name>
<surname>Tareen</surname>
<given-names>A. K.</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>Y.</given-names>
</name>
</person-group> (<year>2023</year>). <article-title>Application of 2D polyoxometalate clusterphene in a high-performance photoelectrochemical photodetector</article-title>. <source>Adv. Opt. Mater.</source> <volume>11</volume> (<issue>20</issue>), <fpage>2300646</fpage>. <pub-id pub-id-type="doi">10.1002/adom.202300646</pub-id>
</citation>
</ref>
<ref id="B120">
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zhang</surname>
<given-names>Y.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>C.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>M.</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>T.</given-names>
</name>
<name>
<surname>Lin</surname>
<given-names>H.</given-names>
</name>
<name>
<surname>Huang</surname>
<given-names>C. B.</given-names>
</name>
<etal/>
</person-group> (<year>2024</year>). <article-title>Attention is all you need: utilizing attention in AI-enabled drug discovery</article-title>. <source>Briefings Bioinforma.</source> <volume>25</volume> (<issue>1</issue>), <fpage>bbad467</fpage>. <pub-id pub-id-type="doi">10.1093/bib/bbad467</pub-id>
</citation>
</ref>
</ref-list>
</back>
</article>