Tong Wu1
Wentao Jiang2*- 1School of BioSciences, Faculty of Science, University of Melbourne, Parkville, VIC, Australia
- 2School of Stomatology, Shandong Second Medical University, Weifang, China
Oral diseases, including dental caries, periodontitis, oral cancer, and mucosal infections, significantly impact overall health, underscoring the need for effective drug development. However, the discovery of novel oral drugs remains challenging due to complex disease mechanisms and limitations in traditional drug screening methods. Computer-aided drug design (CADD) has emerged as a powerful technology to accelerate drug discovery by improving efficiency and reducing costs. This review explores the application of CADD in the development of peptide-based drugs, small molecules, and plant extracts for oral diseases. It discusses CADD-associated antibacterial, anti-inflammatory, anticancer, and tissue regeneration therapies, highlighting available models, online tools, and successful case studies. Additionally, this review examines the intersection of CADD with natural product-based drug discovery, expanding therapeutic possibilities. While CADD enhances drug discovery, challenges such as mismatches in virtual screening and the need for experimental validation remain to be overcome. Despite these limitations, CADD is gaining traction in oral medicine, with the potential to revolutionize treatment strategies. This review aims to inspire further research and promote innovative therapeutic approaches to improve oral health and patient outcomes by summarizing recent advancements and emerging trends.
1 Introduction
The oral cavity serves not only as the starting point of the digestive system but also as a critical site for the manifestation of various diseases. The oral cavity has unique diseases, such as dental caries, periodontitis, pulpitis, and periapical periodontitis. Additionally, studies have shown a close relationship between oral health and overall health, as oral diseases can impact distant organs through mechanisms such as chronic inflammation, bacteremia, and immune system responses (Ferreira et al., 2015; Gupta et al., 2022). For example, they may increase the risk of cardiovascular diseases or be associated with the pathogenesis of Alzheimer’s disease. Furthermore, oral diseases affect mental health and social functions, potentially leading to anxiety, low self-esteem, and reduced quality of life (Gupta et al., 2022).
The design of oral drugs relies primarily on traditional small-molecule drug development, including empirical methods and chemical synthesis-based approaches. However, this conventional strategy often takes long screening cycles, high research and development costs, and low success rates, making large-scale clinical applications challenging (Humphrey et al., 2008). Additionally, research on oral disease drugs has lagged behind that in other medical fields because of the complexity of oral microenvironment and specificity of local drug delivery methods (Kamer et al., 2008). Thus, improving the efficiency of oral drug development remains a key challenge. In terms of pathology, dental caries is primarily driven by Streptococcus mutans, a cariogenic bacterium with strong acidogenic and biofilm-forming capabilities (Loesche, 1986; Bowen and Koo, 2011). whereas Porphyromonas gingivalis is a major pathogenic species in periodontitis (Coats et al., 2009). Oral inflammation and oral cancers are further linked to dysregulated host signaling pathways, including MAPK, NF-κB, and PI3K-Akt cascades (Liu Y. et al., 2024). These concise notes on etiology provide essential biological context for understanding how CADD-based strategies can be applied to oral disease therapeutics.
CADD uses computational methods such as molecular docking, molecular dynamics (MD) simulation, and virtual screening (VS) to efficiently predict drug‒target interactions, significantly reducing development time and improving success rates. Moreover, advancements in artificial intelligence (AI) and machine learning (ML) have further enhanced the predictive capabilities of CADD, increasing the precision of drug screening and optimization (Shoichet, 2004). Consequently, the application of CADD in the development of oral disease drugs holds excellent promise, accelerating the discovery of novel therapeutics and enhancing treatment efficacy. This review explores the application of CADD in the development of drugs for oral diseases, examining its potential, challenges and future advancements (Figure 1). Moreover, this review provides lists of easily accessible models useful for the drug development for oral diseases.
Figure 1. A schematic illustration of this review, which is intended to discuss CADD technologies and related drugs in oral disease treatment.
Nevertheless, despite these advances, current research efforts remain unevenly distributed, with most studies focusing on antibacterial strategies while neglecting anti-inflammatory, antitumor, and tissue regeneration approaches, highlighting the urgent need for a more critical and comprehensive evaluation of CADD applications in oral diseases.
2 What is CADD?
CADD technology utilizes computer techniques and computational methods to accelerate and optimize the drug development process (Lavecchia and Di Giovanni, 2013). It simulates the structure, function, and interactions of target molecules with ligands to screen, design, and optimize potential drug compounds. These approaches often integrate molecular docking, molecular dynamics simulations, pharmacophore modeling, and virtual screening, which allow researchers to explore binding conformations, calculate binding affinities, and evaluate molecular stability under near-physiological conditions (Meng et al., 2011). By narrowing down the number of experimental candidates, CADD not only reduces research costs and development cycles but also improves the precision of hit identification and lead optimization (Schneider, 2018). This article covers various CADD techniques, including ligand-based drug design (LBDD), structure-based drug design (SBDD), artificial intelligence-driven drug discovery (AIDD), and other related methods. LBDD guides drug optimization and novel drug design by studying the structure-activity relationships (SARs) of known ligands. Methods include quantitative structure-activity relationship (QSAR), which predicts the activity of new molecules on the basis of mathematical models that correlate chemical structures with biological activity (Yu and MacKerell, 2017). ML models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), are used to generate new compounds with desired properties (Sanchez-Lengeling and Aspuru-Guzik, 2018). SBDD leverages the three-dimensional structural information of macromolecular targets to identify key binding sites and interactions, designing drugs that can interfere with critical biological pathways (Anderson, 2003). Additionally, molecular dynamics can refine docking results by simulating atomic motions over time, while pharmacophore models provide generalized interaction patterns that facilitate the identification of novel scaffolds (Yang, 2010). Consensus or hybrid-based drug design combines multiple strategies, such as integrating LBDD and SBDD, to overcome the limitations of individual approaches. By leveraging complementary methods, it improves prediction accuracy and enhances the robustness of hit identification (Scior et al., 2012).
In this context, computer-aided drug discovery (CADD) serves as the broader computational framework, within which AI-driven drug discovery (AIDD) has emerged as an advanced subset that explicitly integrates artificial intelligence (AI) and machine learning (ML) into key steps such as candidate generation, ranking, and drug–target interaction prediction. Thus, AIDD represents the progression from traditional computational methods toward more intelligent and adaptive paradigms, embedded within the overarching CADD framework. Classical CADD techniques include molecular docking, which predicts the binding modes of small molecules to targets, and virtual screening (VS), which computationally filters large compound libraries to identify candidates with desired activity profiles (Yu and MacKerell, 2017). High-throughput virtual screening (HTVS) extends these approaches by combining docking, pharmacophore modeling, and free-energy calculations to enhance efficiency (Meng et al., 2011; Lionta et al., 2014). when AI/ML is incorporated to pre-filter compounds or re-rank docking results, HTVS exemplifies the application of AIDD within CADD. Additional pillars include molecular dynamics (MD) simulations and free-energy protocols for pose refinement, as well as ligand-based modeling approaches such as quantitative structure–activity relationship (QSAR) and pharmacophore analysis for activity inference. Network pharmacology (NP) further integrates systems-level biological data with CADD outputs to elucidate mechanisms, identify novel targets, and design multitarget drugs (Hopkins, 2008; Luo et al., 2020). When enhanced with AI—for tasks such as network construction, multi-omics integration, or mechanistic inference these pipelines likewise exemplify AIDD embedded within CADD (Zhou et al., 2019; Li and Zhang, 2013). As illustrated in Figure 2, although these computational models generate theoretical predictions, they often do not fully match experimental results. Similar discrepancies have been reported in other studies. In this study, 63 APRs were identified from the S. mutans proteome, and 54 peptides were synthesized, but only three (C9, C12, and C53) displayed significant antibacterial activity (Chen et al., 2024).This highlights a recurring gap across studies: while computational screening provides valuable hypotheses, many predicted hits remain theoretical, overly complex to validate, or even impossible to confirm experimentally. Taken together, this layered view (CADD → AIDD → technique-level tools) provides the conceptual basis for subsequent discussions of established oral applications (Section 2) and translational prospects (Section 3).
Figure 2. An instance for how CADD-associated online tools help improve the research. The physicochemical properties, structures and biological activities of a group of peptides were predicted with the online tools.
2.1 Widely used structure-prediction models for peptides/proteins
AlphaFold is a popular deep learning model developed by DeepMind to predict the three-dimensional structure of proteins, addressing the long-standing protein folding problem (Institute, EB, 2025). The model leverages amino acid sequences, evolutionary information, multiple sequence alignments (MSAs), coevolutionary data, template sequences, and structural information collaboratively through a multitrack attention mechanism, significantly improving prediction accuracy (Yu and MacKerell, 2017). The latest version, AlphaFold 3, has enhanced the prediction of protein interactions with other biomolecules, providing a new tool for studying complex biological processes and disease mechanisms (Booth, 2024). An analysis of the precise structure of PD-1 has optimized the Dostarlimab antibody used to treat endometrial cancer and other tumors (Canon et al., 2019). It has also aided in understanding KRAS conformational changes, improving the design of the KRAS G12C inhibitor Sotorasib (Canon et al., 2019). Additionally, AlphaFold has resolved the active site structures of EGFR mutations, enhancing the efficacy of the breast cancer drugs Erlotinib and Gefitinib (Zhang et al., 2006). In diabetes treatment, AlphaFold has revealed the three-dimensional structure of the GLP-1 receptor, optimizing the targeting of Semaglutide (Zhang et al., 2017).
RaptorX predicts residue-residue contact probabilities through MSA, enabling accurate modeling of the three-dimensional structures of proteins without homologous templates (Raptor, 2025). It can identify active sites and optimize drug design, such as improving multitarget kinase inhibitors (e.g., Lenvatinib) in cancer drug development to block tumor-related protein activity (Shaikh et al., 2021; Ramsay et al., 2018). DeepAccNet is used to assess and optimize protein structure quality, excelling in the quantitative analysis of local accuracy (Hiranuma et al., 2021). Improving protein structure prediction and facilitating the screening of candidate drugs such as remdesivir and GS-441524 plays key roles in the development of antiviral drugs for the treatment of COVID-19 (Zhuang and Ibrahim, 2021; Kim et al., 2021). ESMFold accelerates drug development with lower computational resources, aiding target identification and vaccine design in COVID-19 research. Its high prediction accuracy is advantageous for studying complex proteins such as protein kinases and farnesyltransferases (Herrington et al., 2024).
2.2 Easily accessible online models for peptides
Currently, in addition to large commercial models, numerous online small-scale model resources are available for researchers. As shown in Table 1, the online model resources for peptides/proteins are listed. These tools can conveniently assist in the research of peptide/protein drugs.
Table 1. Easy accessible online CADD-associated models for proteins or peptides.
This review demonstrates how online tools optimize peptide research using a published study as an example (Figure 2) (Xie et al., 2025). In that study, 10 peptides were designed and analyzed via computational tools such as PepCalc, Heliquest, and NovoPro. Helical wheel diagrams revealed that GKLIKWLLKRSR, KLIWKLLKRSR-NH2, and KWRRWWWKRSR-NH2 lacked the amphipathic characteristics typical of antimicrobial peptides (AMPs), which was confirmed by antimicrobial susceptibility tests (Xie et al., 2025). WL14 and GL14 displayed potent antimicrobial activities, although they possessed abnormally predicted H and μH values. Interestingly, apart from GK16, peptides meeting the predicted criteria failed to show antimicrobial properties, highlighting discrepancies between theoretical predictions and experimental outcomes. Further analysis using the models of W. Tang et al. failed to predict the antimicrobial properties of WL14, GL14, and GK16 (Tang et al., 2022). In contrast, Gronning’s model revealed antibacterial but not antifungal activity (Grønning et al., 2021). Additional computational tools, such as PEP-FOLD and I-TASSER, provided deeper insights into the ability of WL14 to bind calcium ions and regulate host immune defense. These predictions were experimentally validated and helped develop a functionalized biomaterial with potent in vivo activity.
Overall, this study underscores the potential of CADD in peptide research while acknowledging its inherent limitations. While CADD provides a powerful framework for initial screening, it primarily relies on predictive models that are often inadequate for capturing the complexity of proteins and peptides with intricate three-dimensional conformations. At present, most algorithms are restricted to predictions of linear peptide sequences and lack the capacity to accurately model structurally complex or conformationally flexible peptides. Moreover, the predictive performance is constrained by parameter settings, limited training datasets, and oversimplified assumptions, which may lead to discrepancies between in silico predictions and actual biological activities. Consequently, some effective peptides may be overlooked, while others predicted to be active may ultimately prove ineffective when validated experimentally.
2.3 Widely used structure-prediction models for small molecules
AutoDock Vina is a molecular docking tool designed to predict the binding modes and affinities of small molecules with target proteins. It is widely utilized in VS and lead compound optimization. For example, in the screening of COVID-19 inhibitors, potential inhibitors such as Remdesivir metabolites have been identified (Trott and Olson, 2010). The Schrödinger Suite is an advanced molecular simulation and drug design platform for molecular modeling, VS, and MD studies (Schrödinger, 2025).
2.4 Easily accessible online models for small molecules
This section also covers commonly used online predictive models for small molecules (Table 2). These models integrate big data and ML techniques, significantly increasing the efficiency of candidate compound screening and optimization, thereby providing robust support for drug discovery and development. In NP research, online predictive models are widely used for molecular property prediction, activity assessment, and VS, significantly improving the efficiency of candidate compound selection and optimization and thus providing strong support for drug discovery and development. For example, a study on the potential therapeutic mechanism of Panax ginseng for periodontitis utilized SwissTargetPrediction to predict the targets of its active compounds (Sun et al., 2024). This study revealed its potential role in immune regulation and anti-inflammatory effects by integrating NP and molecular docking analyses. Another study on Pueraria flowers and Hovenia dulcis in the treatment of alcohol-induced liver injury combined SwissTargetPrediction target prediction with protein–protein interaction (PPI) network analysis and GO and KEGG pathway enrichment analysis to investigate their therapeutic potential (Wang et al., 2020). These studies highlight the crucial role of online predictive tools in NP research by facilitating the mechanistic exploration of traditional Chinese medicine (TCM) and providing valuable data support for modern drug research.
Table 2. Easy accessible online CADD-associated models for small molecules.
Specific tools possess broad applicability, can predict peptide-related properties, and are suitable for small molecule analysis, exhibiting significant functional advantages for both research (Table 3).
Table 3. Easy accessible online CADD-associated models for proteins and small molecules.
3 The application of CADD in oral diseases
By employing CADD models in combination with database-based online tools, it is possible to accurately predict the conformations of proteins, peptides, and small molecules, thereby providing crucial technical support for novel drug screening. Building on this foundation, the present review systematically analyzes the potential value of these technologies and their research progress in drug development for oral diseases. In particular, we focus on the applications of CADD that have already been established in the oral field. Numerous CADD models have been applied to the design and optimization of diverse molecular drugs, including peptides, small molecules, and other bioactive compounds. These agents have been investigated for their antimicrobial, anti-inflammatory, and antitumor activities, targeting diseases such as dental caries, periodontitis, and oral squamous cell carcinoma. Accordingly, this review provides a systematic summary of CADD-based drugs that have been applied in the context of oral diseases.
3.1 Peptides
3.1.1 Antimicrobial peptides
A wide range of oral diseases are closely associated with bacterial infections. Dental caries is a chronic, cumulative bacterial infectious disease, with S. mutans being the primary cariogenic pathogen. This bacterium exhibits acid production, acid tolerance, and biofilm-forming capabilities, making the development of antimicrobial compounds that target S. mutans a prominent research focus (Loesche, 1986; Bowen and Koo, 2011). In addition to dental caries, periodontitis is another highly prevalent chronic inflammatory oral disease and is a leading cause of tooth loss in adults. Porphyromonas gingivalis is recognized as the principal pathogenic bacterium in periodontitis and is characterized by its ability to invade host tissues, disrupt periodontal support structures, and evade immune responses (Hajishengallis, 2015). Among the various antimicrobial agents, AMPs have emerged as a research focus because of their broad-spectrum antimicrobial activity, low propensity for inducing resistance, and immunomodulatory functions (Wang et al., 2016).
Dongru Chen et al. utilized CADD technology to screen 14 hexapeptides from the sequence of the surface adhesion protein C123 of S. mutans and successfully synthesized 13 for experimental validation (Chen D. et al., 2021). They employed ZipperDB to evaluate whether each six-amino acid sequence could form amyloid fibrils. Tango was also used to predict the β-sheet propensity of peptides, assessing their potential for amyloid fibril formation. Moreover, Waltz was applied to evaluate amyloidogenicity, particularly for short peptide sequences. Five hexapeptides (P1, P3, P6, P7, and P13) were confirmed to have amyloid-forming capabilities. The experimental results demonstrated that these hexapeptides could bind to the cell wall components of S. mutans, thereby triggering amyloid fibril aggregation and subsequently inhibiting biofilm formation (Chen D. et al., 2021). Furthermore, these hexapeptides exhibit broad-spectrum antibiofilm activity against other gram-positive bacteria, such as Streptococcus sanguinis, gram-negative bacteria, such as Escherichia coli, and fungi, such as Candida albicans (Yang L. et al., 2023).
Chen Yucong et al. utilized the TANGO algorithm to screen the proteome of S. mutans and identified 63 seven-amino-acid-long aggregation-prone regions (APRs). These APRs were further compared via BLASTp to ensure that they matched multiple proteins within the S. mutans proteome (Chen et al., 2024). Among them, C9 and C12 exhibited significant antibacterial activity. Their mechanism of action includes inserting into the bacterial membrane, causing membrane disruption and increased permeability, as well as inducing intracellular protein aggregation, thereby accelerating bacterial cell death (Chen et al., 2024).
AMPs have been rarely explored for their efficacy against periodontal pathogens, particularly P. gingivalis, which may be associated with their secretion of proteases. By degrading protein-based therapeutics, these enzymes serve as key factors in the destruction of periodontal tissues and play crucial roles in conferring resistance to AMPs (Coats et al., 2009). Moreover, CADD helps screen for adequate AMPs that target P. gingivalis, suggesting the value of CADD in antimicrobial therapy.
Zarin Taj conducted a study to identify and optimize an AMP from Lactobacillus sp. via in silico approaches. They initially extracted 67 peptide sequences from multiple databases, including DRAMP (http://dramp.cpu-bioinfor.org/), dbAMP (https://awi.cuhk.edu.cn/dbAMP/), and DBAASP (https://dbaasp.org/home) (Taj and Chattopadhyay, 2024). These peptides were screened on the basis of their toxicity, bioactivity, and antibiofilm properties, leading to the selection of 12 candidate peptides. Among the selected peptides, plpl_18 was identified as the most promising AMP for targeting the periodontal pathogens, P. gingivalis and Fusobacterium nucleatum. Structural modeling and molecular docking studies confirmed its strong binding affinity for the virulence proteins RagB and Fap2, which play crucial roles in colonization and biofilm formation. MD simulations further validated the stability and specificity of plpl_18 in binding to these targets, suggesting its potential to inhibit key bacterial functions and reduce pathogenicity. Finally, wet laboratory experiments supported the in silico findings, highlighting the therapeutic potential of plpl_18 as an effective antimicrobial agent against periodontal pathogens (Taj and Chattopadhyay, 2024).
In addition to P. gingivalis and S. mutans, other clinically relevant pathogens, such as Staphylococcus aureus, also contribute to oral diseases, including oral mucositis and osteomyelitis (Peters et al., 2012; Jakubovics and Kolenbrander, 2010). A Protegrin 1-derived peptide, currently in a phase III clinical trial for oral mucositis, exemplifies the application of CADD in antimicrobial peptide optimization. Using QSAR models to predict antimicrobial activity, and ML algorithms such as support vector machines (SVM) and random forest (RF) to refine sequence selectivity and stability, these peptides achieve enhanced efficacy with reduced toxicity. Further improvements are achieved through genetic algorithms, de novo design, and pattern insertion strategies, which optimize antimicrobial potency and membrane-disrupting ability. Similarly, PAC-113 has been optimized with CADD tools to increase activity and stability while minimizing cytotoxicity. Molecular dynamics (MD) simulations validate the interactions of these peptides with bacterial membranes, ensuring both efficacy and safety. Collectively, these computational techniques have accelerated the development of novel AMPs, offering promising candidates for clinical applications (Cardoso et al., 2019).
Peptide drug development has advanced considerably over the past decade, driven by innovations in production, modification, and analytical technologies. Both chemical and biological methods, together with novel design and delivery strategies, have helped to address the inherent limitations of peptides and sustain progress in this field (Wang et al., 2022).In the context of oral diseases, computer-aided drug design (CADD) has facilitated the exploration of antimicrobial peptides (AMPs), particularly in infectious conditions. However, their application to oral inflammation and oral cancer remains minimal, leaving significant opportunities for further investigation. These peptides not only hold promise for modulating the oral inflammatory microenvironment and suppressing pathogenic infections, but also show potential as novel therapeutic strategies for oral malignancies. Despite this promise, no studies to date have reported the use of CADD-based peptides in oral inflammation, oral cancer, or other oral diseases, underscoring the urgent need for systematic research in this area.
3.2 Small molecules
In addition to AMPs, small-molecule compounds have emerged as another research focus. These compounds offer several advantages, including high structural diversity, ease of chemical modification, enhanced stability, and relatively low production costs. These properties underscore their significant potential in the development of anti-infective therapeutics. Unlike CADD-designed peptides lacking anti-inflammation and anticancer studies, CADD-based small molecules have demonstrated research potential not only in antimicrobial applications but also in anti-inflammatory and anticancer applications (Yang et al., 2018). For example, small molecule compounds can be designed to exert specific anti-inflammatory effects or to target the tumor microenvironment for antitumor efficacy (Zhong et al., 2020).
3.2.1 Small molecules against oral pathogens
In the study of dental caries, antibacterial small molecules targeting S. mutans represent a significant direction in CADD-driven drug development (Liu P. et al., 2024; Hwang et al., 2017). Chen et al. utilized CADD for structure-based virtual screening (SBVS) to identify potential inhibitors targeting the C3 fragment of S. mutans from approximately 220,000 small molecules in the Specs database. Using molecular docking in MOE software, they calculated binding energies and applied Lipinski’s rule to filter the top 99 compounds with the highest binding affinities (Chen Y. et al., 2021). D25 exhibited strong inhibitory effects on S. mutans biofilm formation with minimal effects on commensal bacteria, such as Streptococcus gordonii and S. sanguinis, demonstrating good selectivity (Chen Y. et al., 2021). Further investigations revealed that D25 interferes with the interaction between the C3 fragment and A3VP1, leading to the formation of amorphous aggregates, disrupting the structural integrity of amyloid fibrils and ultimately destabilizing the biofilm. Transmission electron microscopy (TEM) revealed that D25 treatment made the amyloid fibrils surrounding S. mutans cells sparse and structurally abnormal. Additionally, srtA and pacR gene expression levels were significantly upregulated after D25 treatment, suggesting that D25 influences amyloid fibril formation through genetic regulation (Chen Y. et al., 2021).
Kanumuru et al. investigated the inhibitory effects of imidazole quinoline derivatives on Gingipain R, a major virulence factor of P. gingivalis. This study employed molecular docking to evaluate the binding affinity of these compounds and utilized SwissADME and ProTox II to predict their pharmacokinetic properties and toxicity risks (Reddy et al., 2023). Through AutoDock Vina, protein-ligand molecular docking was conducted to determine the binding modes of the imidazole quinoline derivatives 1-6. The results showed that compounds 2, 3, and 6 exhibited strong binding affinities, forming stable hydrogen bonds and hydrophobic interactions with the target protein Gingipain R. Additionally, SwissADME predictions confirmed that all the compounds adhered to Lipinski’s rule of five, indicating good oral drug development potential and demonstrating no blood-brain barrier permeability with a lower likelihood of central nervous system side effects. ProTox II toxicity assessment revealed no cytotoxicity, although some compounds exhibited hepatotoxicity or immunotoxicity.
Paul P. et al. aimed to identify small-molecule compounds capable of binding to the bacterial enzyme Pth1 (peptidyl-tRNA hydrolase) as identified through molecular docking simulations. Pth1 is crucial for bacterial survival but is nonessential in human cells, making it a promising target for oral antibacterial drug development (Reddy et al., 2023). Using virtual molecular docking screening, researchers have utilized existing crystal structure data for Pth1 and Pth2 to calculate the binding energies of various antibiotic molecules with these enzymes and rank the results accordingly. Some of the screened compounds, such as Cefixime and Cefoperazone, demonstrated broad-spectrum inhibitory potential across multiple Pth1 enzymes. Others exhibited narrow-spectrum inhibition, with selectivity toward specific bacterial Pth1 enzymes. For example, doxycycline was found to be selective for Acinetobacter baumannii Pth1. Moreover, most small molecules exhibited a significant ability to differentiate between Pth1 and Pth2, suggesting a reduced likelihood of off-target effects on human cellular enzymes. Molecular docking analysis revealed that different bacterial species exhibited subtle structural variations in their Pth1 enzymes, allowing small molecules to bind to these differences selectively. Furthermore, the core structures found in many antibiotics, such as the β-lactam ring in cephalosporins, may mimic the natural substrate of Pth1, thereby increasing its binding affinity. The development of Pth1-targeting drugs holds significant potential for the treatment of oral diseases. Oral infections such as gingivitis and periodontitis frequently involve Gram-positive and Gram-negative bacteria. Pth1 inhibitors could provide targeted antibacterial therapy with minimal impact on human cells (Reddy et al., 2023). Additionally, these compounds can be designed to exhibit either broad-spectrum or narrow-spectrum inhibitory activity, depending on therapeutic needs, thereby increasing treatment efficacy while mitigating antibiotic resistance. Furthermore, combining these novel inhibitors with existing treatments may prolong the effectiveness of current therapies by delaying the emergence of resistance.
Antimicrobial small molecules are a hot topic in CADD for oral applications. In addition to those mentioned above, many other oral CADD antimicrobial small molecules are listed in Table 4.
Table 4. Small molecules that can be used for oral diseases studied with CADD-associated technology.
Similar to CADD-designed peptides, studies on anticancer and anti-inflammatory CADD small molecules in oral research are less prevalent. This finding highlights the research gap and potential value of anticancer and anti-inflammatory small molecules.
3.2.2 Antitumor small molecules
Soykan Agar et al. designed and validated a novel anticancer drug, IHNOCS, through molecular docking and MD simulation for inhibiting head, neck, and oral cancers. IHNOCS targets TGF-β and KRTAP2-3, preventing cancer cell migration while avoiding the uncontrolled proliferation that may result from excessive TGF-β inhibition. The study utilized AutoDock Vina for molecular docking calculations to screen the optimal binding model. Furthermore, Schrödinger’s Desmond was employed to conduct the MD simulation, verifying the stability of the drug‒protein complex. Hydrogen bond mapping analysis further confirmed that IHNOCS remains stable at pH 5.0, the tumor microenvironment and can form stable hydrogen bonds with target proteins. These results indicate that IHNOCS reduces the risk of cancer cell metastasis by partially modulating TGF-β while inhibiting KRTAP2-3. Its targeted action on oral cancer-related proteins suggests its potential therapeutic value for oral cancer treatment (Agar et al., 2024).
3.2.3 Anti-inflammatory small molecules
Pradeep et al. identified key genes and proteins associated with epithelial-mesenchymal transition in Hertwig’s epithelial root sheath, utilizing a systems biology approach, PPI network analysis, and molecular docking techniques. The screening process involves extracting relevant gene and protein lists from the literature, constructing a PPI network via the STRING database, and identifying hub genes within the network (Yadalam et al., 2022). Ultimately, DYRK1A was determined to be a critical target. Modulating DYRK1A expression can attenuate inflammatory responses, providing a foundation for tissue regeneration and repair. Isoetharine was identified as a potential therapeutic candidate through molecular docking and MD simulations and exhibited stable binding with DYRK1A. These findings suggest the potential of Isoetharine as a promising drug candidate for periodontal regeneration (Yadalam et al., 2022).
3.3 Plant extracts
Here we synthesize studies where plant extract–based pipelines have already been investigated in oral contexts.
Plant extracts serve as valuable repositories for natural drug development and represent a focal point in preventing and treating oral diseases. By employing CADD techniques such as NP, molecular docking, VS, and MD simulations, the interactions between the active components of plant extracts and their targets can be systematically analyzed. These approaches enable a scientific evaluation of the therapeutic potential of plant extracts in oral applications, including their antibacterial, antitumor, and anti-inflammatory effects (Table 5). Integrating these techniques enhances drug screening efficiency and provides crucial theoretical support for the development of effective and safe plant extract-based oral therapeutic products.
Table 5. Plant extracts that can be used for oral diseases studied with CADD-associated technology.
Juan et al. explored the molecular mechanisms underlying the therapeutic effects of curcumin on dental caries and its impact on S. mutans. By integrating NP and molecular docking techniques, multiple databases (e.g., PubChem, GEO) were utilized to identify potential targets and differentially expressed genes associated with dental caries. Intersection analysis revealed 134 common targets (Guzmán-Flores et al., 2024). Further investigation revealed that curcumin primarily affects seven key proteins, including MAPK1, BCL2, and KRAS, which are closely related to host immunomodulation. Moreover, metabolic network analysis revealed that curcumin influences 11 metabolic pathways, such as fatty acid metabolism, pyrimidine metabolism, and DNA replication in S. mutans, thereby inhibiting bacterial growth and survival. These findings suggested that curcumin exerted therapeutic potential in treating dental caries by modulating multiple pathways in both the host and the pathogen (Guzmán-Flores et al., 2024).
Hui et al. employed NP and MD techniques to investigate the anticancer potential of quercetin on oral cancer (Dong et al., 2023). Initially, 190 quercetin-related targets were identified via the TCMSP and SwissTargetPrediction databases, whereas 8971 oral cancer-related targets were obtained from the GeneCards and OMIM databases. Intersection analysis yielded 172 potential targets, which were further screened via a PPI network and Cytoscape software and ultimately identified six core targets: AKT1, PIK3R1, MYC, HIF1A, SRC, and HSP90AA1 (Dong et al., 2023). Molecular docking results demonstrated strong binding affinities between quercetin and these targets, indicating its potential as a multitarget anticancer agent.
Yue et al. investigated the mechanism of baicalein in treating periodontitis through NP, molecular docking, and experimental validation. By integrating data from the TCMSP, SwissTargetPrediction, and GeneCards databases, relevant targets for both baicalein and periodontitis were identified, leading to the selection of 17 core targets, including MMP9, TNF-α, and HIF1A. GO and KEGG analyses revealed that baicalein likely exerted its effects via the MAPK, HIF-1, TNF, and PI3K-Akt signaling pathways. Molecular docking demonstrated favorable binding affinities between baicalein and multiple targets. Experimental validation further confirmed that baicalein significantly reduces the expression of TNF-α and MMP-9 induced by P. gingivalis-LPS and inhibits the expression of the M1 macrophage marker iNOS, thereby mitigating inflammatory responses (Liu Y. et al., 2024).
In summary, the application of CADD in oral medicine has made notable progress, encompassing research areas such as oral infections, inflammation, tumors, and tissue regeneration. However, current studies predominantly focus on developing antibacterial agents, whereas research on anti-inflammatory, antitumor, and tissue repair applications remains relatively limited. This disparity suggests that these underexplored areas hold substantial research potential and further developmental value. The uneven distribution of research efforts may be attributed to the varying scope and specificity of medical needs. Compared with oral-specific bacterial infections, such as dental caries and periodontitis, the need for anti-inflammatory, antitumor, and tissue repair interventions extends beyond the oral cavity. They are widely relevant to systemic and other medical subfields. Consequently, research in these domains is often dispersed across broader medical disciplines rather than forming concentrated research hotspots within oral medicine. To systematically explore the potential applications of CADD in oral medicine, we reviewed and synthesized advancements in CADD research related to anti-inflammatory, antitumor, and tissue repair therapies in other medical subfields. By extracting valuable insights from these studies, we aimed to assess their applicability and translational potential in oral medicine.
4 Potential CADD-designed drugs for oral diseases
As noted earlier, although a number of studies have investigated the use of peptides in oral diseases, important gaps remain. For instance, peptides currently applied to the treatment of oral diseases are largely limited to antimicrobial peptides, with little progress in the development of anti-inflammatory or antitumor peptides. In light of this, we further reviewed studies in which CADD-based drug design has been applied in other areas of medicine, and systematically analyzed potential agents by drawing parallels between these diseases and oral conditions (Figure 3). We hope that the design concepts and scientific methodologies established in other disciplines may provide valuable inspiration for the development of novel therapeutics for oral diseases.
Figure 3. Mapping of therapeutic agents from original applications to oral diseases via mechanistic insights.
4.1 Peptides
CADD-designed peptides have been explored in antimicrobial, anti-inflammatory, and antitumor research fields. Notably, advancements in these interdisciplinary studies have deepened fundamental understanding of biological processes and improved translational value for applications in oral healthcare, including the development of innovative diagnostic tools, targeted therapeutics, and biomaterial-based solutions for treating dental and periodontal diseases (Table 6).
Table 6. Peptides with potential for oral diseases studied with CADD-associated technology.
4.1.1 Antibacterial peptides
4.1.1.1 CaP-5, CaP-9
Zhang et al. initially employed CADD technology to screen a series of short cationic peptides targeting C. albicans infection, and evaluated their stability and safety using AntiBP2 and ToxinPred. Molecular docking and dynamics simulations showed strong binding of these peptides to the fungal cell wall adhesin Als3, while experimental validation confirmed that CaP-5 and CaP-9 significantly inhibited C. albicans adhesion and biofilm formation, and were also effective against mixed biofilms of C. albicans and S. mutans. Since common oral infections such as caries, denture stomatitis, and oral candidiasis are closely associated with mixed biofilms, these peptides demonstrate the ability to act on both fungi and bacteria, disrupt complex microbial communities, and thus hold strong potential for application in oral antimicrobial therapy (Awdhesh Kumar Mishra and Kodiveri, 2024).
4.1.2 Anti-inflammatory peptides
4.1.2.1 ARRF and ARNF
ARRF and ARNF were selected via ExPASy Peptide Cutter software, which was employed to simulate the enzymatic actions of trypsin, pepsin, and papain, generating 529 peptide fragments. The biological properties of these peptides were further validated via molecular docking and MD simulation techniques, revealing that hydrogen bonding and hydrophobic interactions play crucial roles in their binding to the Keap1 and TLR4 receptors, with high stability in receptor binding. Experimental validation demonstrated that ARRF and ARNF exhibited significant antioxidant and anti-inflammatory activities in LPS-induced RAW264.7 macrophage models. Specifically, these peptides suppressed the TLR4 signaling pathway, reducing the secretion of inflammatory cytokines such as TNF-α and IL-6. Moreover, they alleviated oxidative stress by decreasing reactive oxygen species (ROS) levels and increasing the activities of antioxidant enzymes, including SOD and GSH-Px. Compared with conventional peptide screening methods, this study significantly improved screening efficiency by integrating virtual enzymatic hydrolysis with molecular simulation techniques. Their anti-inflammatory properties may help mitigate gingivitis and other oral inflammatory conditions. Moreover, their antioxidant effects may offer protective benefits in treating oxidative stress-related oral diseases such as oral ulcers and periodontitis. Additionally, naturally derived functional peptides from food sources exhibit low toxicity and high safety, making them suitable for development into oral health products such as mouth rinses or toothpaste (Xin et al., 2024).
4.1.3 Anti-tumor peptides
4.1.3.1 Cordyceps militaris
AMPs with anticancer potential derived from Cordyceps militaris were screened via pepsin digestion simulations to generate 21,148 peptide sequences, which were then screened for anticancer activity via 3 ML models—AntiCP, iACP, and MLACP. Toxicity and cell penetration capabilities were assessed via ToxinPred and MLCPP to ensure high efficiency and safety. The screened AMPs were further optimized through AntiCP, and their physicochemical properties were refined via PEP-FOLD3.5 molecular modeling. In HT-29 colorectal cancer cell line experiments, the AMPs selectively bound to and disrupted cancer cell membranes due to their cationicity and amphipathic structure, thereby inducing apoptosis and inhibiting tumor proliferation. Notably, given the similarity in membrane charge and biochemical properties between oral and colorectal cancer cells, C-ori may exhibit similar therapeutic effects in oral cancer treatment. Moreover, localized drug delivery systems, such as oral patches or gels, could be utilized for effective therapeutic administration (Zhuang and Ibrahim, 2021).
4.2 Small molecules
CADD-designed small molecules have been explored for their antibacterial, anti-inflammatory, and anticancer effects. Moreover, advancements in these disciplines have translational potential for applications in oral health (Table 7).
Table 7. Small molecules with potential for oral diseases studied with CADD-associated technology.
4.2.1 Antibacterial small molecules
The Bugworks Research Team screened a chemical library comprising approximately 3,000 compounds from commercial databases such as Molecule and Enamine. Initial phenotypic screening was performed using an E. coli model, followed by structural optimization of the identified lead compounds. MD simulations and SAR analyses were employed to refine the compound’s binding mode to the target proteins during the drug optimization process. BWC0977 demonstrated potent antibacterial activity, with a minimum inhibitory concentration ranging from 0.03 to 2 μg/mL against a broad spectrum of MDR pathogens. Additionally, its ability to achieve high drug concentrations in infected tissues, such as pulmonary epithelial lining fluid, suggests that it may offer similar advantages in treating localized tissue infections, including oral infections. This makes it particularly suitable for combating MDR strains in oral infections (Hameed et al., 2024).
4.2.2 Anti-inflammatory small molecules
By using CADD techniques in conjunction with VS and molecular docking, two potential HDAC6 inhibitors with anti-inflammatory properties were identified and validated, A1, 5-(4-bromonaphthalene-1-sulfonamido)-2-hydroxybenzoic acid, and B1, N-(9-oxo-9H-fluoren-3-yl)-benzamide. The study screened over 175,000 compounds from the ZINC15 and OTAVA chemical databases via PyRx software to identify candidates with high binding affinity. This step was followed by further molecular docking optimization to confirm the reliability of their interactions with HDAC6. A1 and B1 effectively downregulate inflammatory cytokines such as IL-6, TNF-α, and IL-1β while suppressing key inflammatory pathways, including the NF-κB pathway. These compounds may demonstrate significant anti-inflammatory effects in treating periodontitis and gingivitis by mitigating inflammatory responses, inhibiting alveolar bone resorption, and promoting periodontal tissue repair. Additionally, HDAC6 inhibitors have been shown to play crucial roles in cell migration and tissue regeneration, suggesting their potential to enhance the healing of oral ulcers (Dawood et al., 2020).
4.2.3 Anti-tumor small molecules
Notably, both A1 and B1 can also induce apoptosis in leukemia cells, including multidrug-resistant CEM/ADR5000 cell lines, indicating their potential anticancer activity and offering a promising avenue for oral cancer treatment (Dawood et al., 2020).
A study utilized VS and MD simulations to confirm the potent dual inhibitory effect of Pitavastatin on hIDO1 and hTDO2. Researchers performed molecular docking to screen potential inhibitors from the DrugBank database and found that Pitavastatin exhibited high binding affinity for both targets. Subsequently, MD simulations were conducted via GROMACS to assess its stability. The simulations employed the GROMOS 54A7 force field. The TIP3P water model was used for solvation, and the PME method was applied for electrostatic calculations. Pitavastatin effectively reduced immune suppression, enhancing immune-mediated clearance of tumor cells. Additionally, Pitavastatin induced G1/S cell cycle arrest, activated the Caspase-3 pathway to promote apoptosis in cancer cells, and downregulated key oncogenic and inflammatory signaling molecules, including STAT3, AhR, and IL-6, thereby suppressing tumor invasion. Given its immunomodulatory, anti-inflammatory, and antitumor effects, Pitavastatin is a promising candidate for oral cancer therapy, paving the way for novel treatment strategies (Aboomar et al., 2024).
4.3 Plant extracts
Here we emphasize extra-oral pipelines and mechanisms that could translate to oral indications. CADD technologies have been widely applied to study the anti-inflammatory and antitumor properties of plant extracts. Furthermore, advancements in these interdisciplinary research areas hold significant translational potential for applications in oral health (Table 8).
Table 8. Plant extracts with potential for oral diseases studied with CADD-associated technology.
4.3.1 Antimicrobial plant extracts
A study explored the potential of resveratrol in alleviating COVID-19-related inflammation through NP methods. Using the TargetNet and Comparative Toxicogenomics Database, 235 resveratrol-associated targets were identified, and 510 differentially expressed genes related to COVID-19 were screened via the GEO dataset. Through PPI network and molecular docking analyses, resveratrol was verified to have strong binding affinity with key targets such as PLAT and MMP13. GO and KEGG pathway analysis revealed that resveratrol regulated inflammation via the IL-17, NF-κB, and TNF signaling pathways, potentially mitigating cytokine storms and acute respiratory distress syndrome. These findings highlight the anti-inflammatory and antimicrobial properties of resveratrol, suggesting its potential application in oral health. Its antioxidant and antifibrotic properties may contribute to oral tissue repair (Xiao et al., 2021).
4.3.2 Anti-inflammatory plant extracts
An integrated approach, including NP, molecular docking, and in vitro/in vivo experiments, was used to investigate the mechanism of naringenin in chronic wound healing. Molecular docking analysis was conducted via CB-Dock2 to identify binding pocket sites, calculate docking scores, and predict binding modes. 163 related targets were identified, which are associated primarily with oxidative stress, inflammation regulation, and metabolic processes and have significant anti-inflammatory and antioxidant effects. The key targets included RELA, AKT1, MAPK1, and MAPK3. These targets are considered crucial for the therapeutic effects of naringenin in chronic wound healing. Molecular docking results indicated that naringenin could bind stably to these targets. In cellular experiments, low concentrations of naringin reduced ROS production and inhibited the expression of inflammatory cytokines such as TNF-α and IL-6 (Sun et al., 2023).
4.3.3 Antitumor plant extracts
A study investigated the mechanism and target of matrine in ovarian cancer via NP, MR, and molecular docking techniques. Through database screening and gene expression analysis, six core targets were identified, including TP53, CCND1, STAT3, VEGFA, IL1B, and CCL2. MR analysis indicated that TP53 and CCND1 were risk factors for ovarian cancer, whereas VEGFA and IL1B exhibited protective effects. Molecular docking confirmed the stable binding of matrine to TP53, CCND1, and IL1B. In vitro experiments further demonstrated that matrine downregulated CCND1 and IL1B expression while upregulating TP53, indicating that matrine possessed significant anticancer effects. These findings provide theoretical support for the potential application of matrine in oral health. The downregulation of IL1B may alleviate oral inflammation, such as periodontitis, while TP53-related mechanisms could inhibit the proliferation of oral pathogens. Additionally, CCND1 regulation may influence the oral microbiome. The antitumor mechanisms of matrine also suggest its potential in treating oral cancers and other pathological conditions (Chen and Song, 2024).
5 Conclusion
This review summarizes the advancements in the application of CADD technologies in drug development for oral diseases. CADD significantly enhances drug screening efficiency through molecular docking, VS, and MD simulations. However, the reliability of potential binding molecules identified solely through CADD remains uncertain. As discussed earlier, despite the identification of numerous high-affinity molecules via computational screening, only a small fraction has been proven effective upon experimental validation. For example, in the development of small-molecule drugs that target S. mutans, although dozens of high-binding-energy candidate molecules have been identified, only a few have exhibited efficacy in experimental validation. This underscores the necessity of experimental confirmation for CADD-derived results. Integrating wet-lab experiments, such as drug sensitivity assays, cellular studies, and animal model validations, is essential to improve the practical utility of CADD-screened molecules. Experimental validation is crucial for assessing the bioactivity and toxicity of identified molecules.
Future progress hinges on bidirectional loops between computation and experiment: standardized assay panels for key oral pathogens and inflammation/cancer models, transparent reporting of negative results to sharpen predictors, and benchmarking datasets tailored to peptide conformational diversity. Such practices will narrow the CADD–AIDD prediction–validation gap and accelerate translation in oral indications.
Furthermore, experimental feedback can be utilized to refine CADD models, thereby improving their predictive accuracy. For example, recent research on AMPs has successfully elucidated their antibacterial mechanisms by combining molecular docking with experimental validation. Similarly, in small-molecule drug development, HTVS coupled with MD simulations has significantly increased the efficiency of candidate selection. Despite existing challenges, CADD has vast potential in drug discovery for oral diseases. From AMPs to small-molecule compounds and AIDD, CADD is facilitating the transition from traditional experience-based drug discovery to a computationally driven paradigm, paving the way for precision therapies in oral diseases.
Author contributions
TW: Conceptualization, Visualization, Writing – original draft, Writing – review and editing. WJ: Conceptualization, Funding acquisition, Visualization, Writing – review and editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was financially supported by the National Natural Science Foundation of China (No. 82100995) and Shandong Provincial Natural Science Foundation (No. ZR2024QH186).
Conflict of interest
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.
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The author(s) declare that no Generative AI was used in the creation of this manuscript.
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Keywords: computer-aided drug design, oral diseases, peptide, small molecule, plant extract
Citation: Wu T and Jiang W (2025) Computational-aided drug design strategies for drug discovery and development against oral diseases. Front. Pharmacol. 16:1678652. doi: 10.3389/fphar.2025.1678652
Received: 11 August 2025; Accepted: 24 October 2025;
Published: 07 November 2025.
Edited by:
Arpana Parihar, Advanced Materials and Processes Research Institute (CSIR), IndiaReviewed by:
Tarik Aanniz, Mohammed V University in Rabat, MoroccoEdgar López-López, National Polytechnic Institute of Mexico (CINVESTAV), Mexico
Manal Moustafa, Badr University in Asyut, Egypt
Copyright © 2025 Wu and Jiang. 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.
*Correspondence: Wentao Jiang, amlhbmd3dDdAc2RzbXUuZWR1LmNu