Synergistic Integration of Artificial Intelligence, Organoid Models, and Multi‐Omics Technologies in Contemporary Drug Discovery

ABSTRACT The integration of advanced biotechnology platforms is revolutionizing the drug discovery landscape, enhancing efficiency and success rates while reducing development costs. This review examines the convergence of artificial intelligence (AI), high‐throughput screening, organoid technology, and multi‐omics approaches in drug development. AI and machine learning algorithms leverage big data to predict drug‐target interactions, optimize molecular structures, and identify novel therapeutic candidates. Organoid‐based in vitro models, complex 3D cellular constructs derived from stem cells, recapitulate human disease biology than conventional 2D cell cultures, improving the predictive power of preclinical efficacy and toxicity testing. High‐throughput phenotypic screening, enhanced by automation, enables testing of vast compound libraries in physiologically relevant cell systems. Multi‐omics technologies (genomics, proteomics, and metabolomics) yield comprehensive molecular profiles of disease states and drug responses. AI‐driven predictions can be experimentally validated in organoid models, while organoid‐derived data feed back into machine learning models to refine predictions. Current challenges, including standardization of organoid culture protocols, validation of AI model predictions, and the management of multi‐modal big data are critically examined. Emerging trends and future directions are presented, highlighting the potential of these integrated approaches to accelerate the development of personalized therapies and reduce attrition rates in clinical trials.