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

Synergistic Integration of Artificial Intelligence, Organoid Models, and Multi‐Omics Technologies in Contemporary Drug Discovery Yuvaraj Dinakarkumar Department of Biotechnology School of Life Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India https://orcid.org/0000-0001-7579-4157 Saravana Kumar Ganesan Department of Computer Science Engineering Karpagam Academy of Higher Education (Deemed University) Coimbatore Tamil Nadu India Rinish Mortin John Department of Biotechnology Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Aishwarya Lakshmi Thasvanth Raj Department of Biotechnology Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Harshaveena Raja Department of Biotechnology Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Arokiyaraj Selvaraj Department of Food Science & Biotechnology and Carbohydrate Bioproduct Research Center Sejong University South Korea N. Punitha Department of Physics St. Joseph's College of Engineering Chennai Tamil Nadu India Venkatesh Alagarsamy Dept. Of Conservative Dentistry & Endodontics Sree Balaji Dental College & Hospital Bharat Institute of Higher Education & Research (BIHER) Chennai Tamil Nadu India 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.
12 24 2025 01 2026 e00532 10.1002/adtp.202500532 2 10.1002/crossmark_policy advanced.onlinelibrary.wiley.com true 2025-10-28 2025-12-11 2025-12-24 http://onlinelibrary.wiley.com/termsAndConditions#vor http://doi.wiley.com/10.1002/tdm_license_1.1 10.1002/adtp.202500532 https://advanced.onlinelibrary.wiley.com/doi/10.1002/adtp.202500532 https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/adtp.202500532 https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/adtp.202500532 https://advanced.onlinelibrary.wiley.com/doi/full-xml/10.1002/adtp.202500532 10.3390/nu16071073 10.5492/wjccm.v13.i1.90176 10.1016/j.jpha.2025.101248 10.3390/pharmaceutics16101328 N. A.Colwell “Harnessing Artificial Intelligence in Drug Discovery and Development 2024 ” accessed July 9 2025 https://www.accc‐cancer.org/acccbuzz/blog‐post‐template/accc‐buzz/2024/12/20/harnessing‐artificial‐intelligence‐in‐drug‐discovery‐and‐development. 10.1021/acs.jnatprod.4c00581 10.1016/j.xphs.2024.06.016 10.3390/ddc3010009 10.1016/j.clinthera.2019.05.018 10.1111/cts.13055 10.1126/sciadv.aap7885 10.1016/j.chemolab.2019.103850 10.1016/j.jmgm.2024.108872 10.1016/j.drudis.2018.11.014 10.1186/s12874-019-0681-4 10.1016/j.plantsci.2019.03.020 10.1038/s41587-019-0224-x 10.1109/TNNLS.2022.3161030 10.1016/j.neunet.2023.12.025 10.1038/s41598-020-78368-1 10.1016/j.drudis.2015.12.007 10.1016/j.jad.2024.08.182 10.1016/j.lfs.2025.123821 10.1186/s13321-025-00995-5 10.1038/s41587-024-02143-0 10.1038/s41591-025-03743-2 10.1039/D4DD00257A 10.1039/9781837676941-00026 10.1016/j.bcp.2023.115770 10.1016/j.cell.2022.10.017 10.1038/s41598-020-69354-8 10.1016/j.mtchem.2022.101021 10.3390/bios13100905 10.1016/j.bioactmat.2023.09.005 10.1080/17460441.2025.2481262 10.1016/j.actbio.2021.06.025 10.1038/s41573-022-00409-3 10.1016/j.aca.2017.08.026 10.1016/j.envpol.2024.124675 10.1038/s41467-024-53432-w 10.1038/s41573-022-00615-z 10.3389/fonc.2020.588221 10.1038/s41576-018-0051-9 10.1016/j.bioactmat.2022.01.048 10.1016/j.jcf.2019.11.002 Recent Patents on Anti‐Cancer Drug Discovery Ray S. K. 80 17 2021 Imitating Hypoxia and Tumor Microenvironment With Immune Evasion by Employing Three Dimensional In Vitro Cellular Models: Impressive Tool in Drug Discovery 10.1002/stem.2852 10.1002/adma.201902042 10.36922/or.8162 10.36922/or.8262 10.36922/OR025040007 10.2533/chimia.2019.81 10.1002/biot.202000463 10.3390/ijms241411427 10.1186/s12967-025-06349-x 10.1016/j.medj.2023.04.003 10.1002/btm2.10641 10.3390/biom14060692 10.1159/000539678 Preclinical Cancer Models for Translational Research and Drug Development In S. P. 153 2025 Hepatoma Research He J. 6 11 2025 Application of Multi‐omics in Hepatocellular Carcinoma: New Prospects for Classification and Precise Diagnosis and Treatment 10.1021/acs.chemrestox.5c00033 10.1093/bib/bbaa122 10.1038/s41575-019-0240-9 10.1016/j.gpb.2022.11.011 10.1016/j.aichem.2024.100072 10.1007/s12015-024-10814-3 10.1089/jop.2018.0140 10.1016/j.tibtech.2017.02.012 10.3389/frai.2025.1681106 SSRN Uddin M. R. 50 2025 Artificial Intelligence and Machine Learning in Pharmaceutical Sciences: Unpacking Regulatory Guidance, Opportunities, and Challenges for Safe and Effective Drug Development 10.3390/ph18010047 Acta Scientific Medical Sciences Gundlapalli S. 24 9 7 2025 The Application of Organ‐on‐a‐Chip Technology for Disease Modeling and Drug Testing 10.1002/ddr.22115 10.1002/qub2.70002 10.1007/s12033-024-01133-6 10.1038/s12276-025-01487-0 Briefings in Bioinformatics Li Y. 1 22 2021 Advances in Bulk and Single‐Cell Multi‐Omics Approaches for Systems Biology and Precision Medicine 10.1038/s41551-020-0565-2 10.1111/bioe.13047 10.1007/s10462-024-10884-2 10.1002/wics.1617 10.1021/acs.chemrev.3c00189 10.3389/fphar.2024.1331062 10.2174/1568026622666221006140825 Clinical Nutrition Bond A. 542 57 2023 Artificial Intelligence & Clinical Nutrition: What the Future Might Have in Store 10.3389/frai.2020.621577 Animal Law Williams J. 139 30 2024 FDA Modernization Act 2.0: The Beginning of the End for Animal Testing in Drug Development 10.1038/d41573-025-00087-x 10.1080/10408444.2021.1953439 10.1039/D2LC00307D 10.1016/B978-0-12-803620-4.00008-6 Medical Devices: Impact on Innovation and Market Access Applied Sciences Amaral C. 9304 14 2024 Global Regulatory Challenges for 10.3390/ph16111556 10.1021/acsptsci.5c00162 Mapping the Landscape of Real‐World Evidence (RWE) Guidance and Initiatives Across Regulatory Jurisdictions Conceição C. M. 2024 10.1080/20016689.2022.2147286 10.1007/s43441-024-00639-0 10.1007/s10115-023-02049-4 10.7717/peerj-cs.584 10.1016/j.mcpdig.2025.100246 Innovation Han X. 100620 5 2024 Landscape of human Organoids: Ideal Model in Clinics and Research 10.1016/S1473-3099(20)30791-X