The integration of artificial intelligence (AI) into drug discovery is revolutionizing pharmaceutical research by providing sophisticated tools for understanding and predicting complex biological interactions. These advancements enable researchers to move beyond traditional methods, bringing unprecedented precision and efficiency to the drug development process. AI techniques such as machine learning, deep learning, knowledge graphs, and large language models are transforming how scientists identify drug candidates and predict their interactions with biological targets. These methods facilitate rapid screening of vast chemical libraries, prediction of molecular targets, and elucidation of mechanisms of action, thereby shortening development timelines and enhancing the accuracy of therapeutic discoveries. Moreover, the combination of AI with experimental validation techniques significantly streamlines the drug discovery pipeline. By improving the prediction of drug efficacy and off-target effects, AI supports the development of more effective and safer drugs. This integration is critical for addressing longstanding challenges in experimental pharmacology, such as optimizing drug specificity and minimizing adverse effects.
This special issue seeks to address the critical challenges and leverage recent advancements in artificial intelligence (AI) to advance drug discovery and mechanism elucidation. Traditional drug discovery methods are often constrained by inefficiencies and high failure rates, necessitating more effective approaches. AI offers promising solutions through sophisticated techniques, such as diffusion models, meta-learning, knowledge graph embeddings, and large language models, to enhance drug screening, predict drug-target interactions, and elucidate mechanisms of action. By focusing on these cutting-edge advancements, the special issue aims to explore how AI can overcome existing limitations, including issues related to data quality, model interpretability, and scalability. It provides a platform for researchers across computational biology, chemistry, and pharmacology to share insights, foster interdisciplinary collaborations, and drive innovation, ultimately enhancing the effectiveness and efficiency of drug discovery and mechanism elucidation.
This Research Topic focuses on integrating artificial intelligence (AI) into experimental pharmacology, with an emphasis on drug-target interactions, mechanisms of action, and drug screening. We invite contributions that explore cutting-edge AI techniques, such as machine learning, deep learning, knowledge graphs, and large language models, within the following themes:
Drug Screening and Repurposing: Applying AI algorithms to enhance drug-target interactions and elucidate mechanisms of action.
Multi-Omics Integration: Leveraging AI to integrate genomic, transcriptomic, proteomic, and metabolomic data for identifying novel therapeutic targets and biomarkers.
AI-Driven Drug Design: Employing AI techniques, including generative models, to advance innovative drug design strategies.
We welcome original research articles, reviews, and case studies that address these themes and contribute to advancing AI applications in experimental pharmacology and drug discovery.
The integration of artificial intelligence (AI) into drug discovery is revolutionizing pharmaceutical research by providing sophisticated tools for understanding and predicting complex biological interactions. These advancements enable researchers to move beyond traditional methods, bringing unprecedented precision and efficiency to the drug development process. AI techniques such as machine learning, deep learning, knowledge graphs, and large language models are transforming how scientists identify drug candidates and predict their interactions with biological targets. These methods facilitate rapid screening of vast chemical libraries, prediction of molecular targets, and elucidation of mechanisms of action, thereby shortening development timelines and enhancing the accuracy of therapeutic discoveries. Moreover, the combination of AI with experimental validation techniques significantly streamlines the drug discovery pipeline. By improving the prediction of drug efficacy and off-target effects, AI supports the development of more effective and safer drugs. This integration is critical for addressing longstanding challenges in experimental pharmacology, such as optimizing drug specificity and minimizing adverse effects.
This special issue seeks to address the critical challenges and leverage recent advancements in artificial intelligence (AI) to advance drug discovery and mechanism elucidation. Traditional drug discovery methods are often constrained by inefficiencies and high failure rates, necessitating more effective approaches. AI offers promising solutions through sophisticated techniques, such as diffusion models, meta-learning, knowledge graph embeddings, and large language models, to enhance drug screening, predict drug-target interactions, and elucidate mechanisms of action. By focusing on these cutting-edge advancements, the special issue aims to explore how AI can overcome existing limitations, including issues related to data quality, model interpretability, and scalability. It provides a platform for researchers across computational biology, chemistry, and pharmacology to share insights, foster interdisciplinary collaborations, and drive innovation, ultimately enhancing the effectiveness and efficiency of drug discovery and mechanism elucidation.
This Research Topic focuses on integrating artificial intelligence (AI) into experimental pharmacology, with an emphasis on drug-target interactions, mechanisms of action, and drug screening. We invite contributions that explore cutting-edge AI techniques, such as machine learning, deep learning, knowledge graphs, and large language models, within the following themes:
Drug Screening and Repurposing: Applying AI algorithms to enhance drug-target interactions and elucidate mechanisms of action.
Multi-Omics Integration: Leveraging AI to integrate genomic, transcriptomic, proteomic, and metabolomic data for identifying novel therapeutic targets and biomarkers.
AI-Driven Drug Design: Employing AI techniques, including generative models, to advance innovative drug design strategies.
We welcome original research articles, reviews, and case studies that address these themes and contribute to advancing AI applications in experimental pharmacology and drug discovery.