Artificial Intelligence and Multi-Omics-Driven Pharmacology: Transforming Drug Discovery, Biomarker Identification and Precision Therapeutics

Recent advances in artificial intelligence (AI) and multi-omics technologies are changing the way pharmacological research is carried out. Technologies such as genomics, transcriptomics, proteomics, metabolomics, epigenomics and single-cell and spatial omics are generating large amounts of biological data that can improve our understanding of disease mechanisms. When combined with AI and machine learning, these data can help identify new drug targets, discover reliable biomarkers and support the development of more effective treatments. These approaches are being applied throughout the drug development process, from identifying potential therapeutic compounds and repurposing existing drugs to improving treatment selection and patient care. Bringing together expertise from computational, biological and clinical sciences will be essential to translate these advances into practical and personalized healthcare solutions.This Research Topic aims to highlight recent advances in AI and multi-omics-driven experimental pharmacology and preclinical drug discovery, with a particular focus on identifying, validating and optimizing novel therapeutic targets and pharmacological agents. We welcome studies integrating artificial intelligence with genomics, transcriptomics, proteomics, metabolomics and systems pharmacology to accelerate target identification, lead discovery, drug repurposing, medicinal chemistry and pharmacokinetic, metabolic and toxicological evaluation. Particular emphasis will be placed on experimental validation of AI-predicted drug targets and pharmacological mechanisms using in-vitro and/or in-vivo models, thereby strengthening the translation of computational predictions into mechanistic pharmacological evidence. The Research Topic seeks to bring together computational scientists, pharmacologists, medicinal chemists, molecular biologists and translational researchers to advance AI-assisted preclinical drug discovery while improving our understanding of drug–target interactions, molecular mechanisms of action and therapeutic efficacy across cancer, infectious, neurological, metabolic, cardiovascular and immune-related diseases.We welcome Original Research, Review, Mini Review, Systematic Review, Methods, Perspective, Opinion and Brief Research Report articles related to, but not limited to, the following topics:-Artificial intelligence and machine learning in drug discovery and drug repurposing-Multi-omics approaches in pharmacological research-Biomarker discovery and validation-Precision medicine and personalized therapies-Systems and network pharmacology-Pharmacogenomics and pharmacoproteomics-AI applications in pharmacokinetic and pharmacodynamic studies-Single-cell, spatial, and other advanced omics technologies-Digital biomarkers and real-world clinical data-Explainable and generative AI for pharmacological research-Experimental validation of AI-predicted drug targets-Clinical applications of AI-assisted pharmacology-Ethical, regulatory, and practical challenges in using AI in pharmacologyEligibility requirements: Every submission must investigate a defined drug-target interaction and its pharmacological effects, supported by experimental validation using in-vitro and/or in-vivo approaches where AI or computational methods are employed. Purely in-silico or computational studies without experimental validation, clinical-trial reports, patient-prognosis studies and manuscripts primarily focused on real-world clinical data are outside the scope of this Research Topic. Topic editors: Mitesh Patel, Mohd Adnan Source: https://www.frontiersin.org/research-topics/83586/artificial-intelligence-and-multi-omics-driven-pharmacology-transforming-drug-discovery-biomarker-identification-and-precision-therapeutics