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The review critically compares five major AI-driven discovery platforms, spanning generative chemistry, phenomics-first systems, integrated target-to-design pipelines, knowledge-graph repurposing, and physics-plus machine learning design. It highlights developments since 2024, including Insilico Medicine’s phase IIa progress for ISM001-055, the Recursion–Exscientia merger, and Schrödinger-related late-stage advancement of TAK-279. It also emphasizes regulatory, ethical, and robotics-enabled translation pathways shaping reliable drug discovery.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/leading-artificial-intelligencedriven-drug-discovery-platforms-2025-landscape-and-global-outlook/505804/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/leading-artificial-intelligencedriven-drug-discovery-platforms-2025-landscape-and-global-outlook/505804.png","ImageObject",300,407,{"name":92,"@type":93},"Pentious","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-10-04",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which five AI-driven drug discovery platforms are compared in the review?","Question",{"text":112,"@type":113},"The review compares Exscientia, Recursion Pharmaceuticals, Insilico Medicine, BenevolentAI, and Schrödinger, covering generative design, phenomics-first discovery, end-to-end generative pipelines, knowledge-graph repurposing, and physics-plus machine learning strategies.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What key post-2024 developments are highlighted for clinical progress?",{"text":117,"@type":113},"It highlights positive phase IIa results for Insilico Medicine’s ISM001-055 (Traf2-and Nck-interacting kinase inhibitor) in idiopathic pulmonary fibrosis, and the Recursion–Exscientia merger integrating phenomic screening with automated precision chemistry. It also notes Schrödinger’s physics-enabled strategy reaching late-stage testing via TAK-279 (zasocitinib) into phase III trials.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the review address regulatory, ethical, and data-related risks?",{"text":121,"@type":113},"It outlines regulatory guidance and focuses on transparency and explainability, data bias and fairness, accountability and validation (including clinical-trial accountability), and intellectual property and privacy considerations.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},505804,1791449081,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1374404730887,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Pharmacological Reviews 78 (2026) 100102  \njournal  \nPharmacological Reviews  \n[homepage:](homepage: www . p ha rm rev . as petjournals . o rg)[ www . p ha rm rev . as petjournals . o rg](homepage: www . p ha rm rev . as petjournals . o rg)  \nREVIEW ARTICLE  \nLeading artificial intelligence—driven drug discovery platforms: 2025 landscape and global outlook  \nMahendiran Dharmasivam*, Busra Kaya, Adedoyin Akinware , Mahan Gholam Azad, Des R. Richardson*   \nCentre for Cancer Cell Biology and Drug Discovery, Institute for Biomedicine and Glycomics, Griffith University, Southport, Queensland, Australia  \nAbstract  2  \nSignificance Statement  2  \nI. Introduction  2  \nA. Exscientia: Generative design and the “Centaur Chemist” approach  3  \n1. Platform progress and 2024—2025 updates  4  \n2. Clinical impact  5  \nB. Recursion pharmaceuticals: High-throughput phenotypic discovery  5  \n1. Platform evolution and 2024—2025 updates  6  \n2. Reflections  6  \nC. Insilico Medicine: End-to-end artificial intelligence and generative successes  7  \n1. Pipeline and 2024—2025 progress  7  \n2. Outlook and significance  8  \nD. BenevolentAI: Knowledge graphs, repurposing, and a cautionary tale  9  \n1. Pipeline and 2024 updates  9  \n2. Analysis  10  \nE. Schr€odinger: Physics-based simulation meets machine learning  10  \n1. Perspective  12  \nF. Comparative summary  12  \nII. Emerging players and global developments (2024—2025)  12  \nA. Insitro (USA)  12  \nB. Isomorphic Labs (UK)  12  \nC. Atomwise (USA)  13  \nD. XtalPi and Chinese artificial intelligence biotech companies (China)  13  \nE. Big pharma’s internal efforts and smaller artificial intelligence startups  14  \nF. Artificial intelligence in biologics and new therapeutic modalities  14  \nG. Global and non-Western pharmaceutical artificial intelligence innovation  14  \nIII. Regulatory and ethical considerations in artificial intelligence—driven drug discovery  15  \nA. Regulatory guidance  15  \nB. Transparency and explainability  15  \nC. Data bias and fairness  15  \nD. Accountability and validation  16  \nE. Another aspect of accountability is in clinical trials  16  \nF. Intellectual property and privacy  16  \nIV. Robotics artificial intelligence in pharmacology: From “Hands-On” to “Self-Driving” discovery  16  \nA. AstraZeneca iLab (Gothenburg)  16  \nB. Recursion (Salt Lake City)  16  \nC. Insitro (South San Francisco)  16  \n* Address correspondence to: Dr Mahendiran Dharmasivam, Centre for Cancer Cell Biology and Drug Discovery, Institute for Biomedicine and Glycomics, Griffith University, Southport, Queensland 4215, [Australia. E-mail:](Australia. E-mail: m.dharmasivam@griffith.edu.au)[ m.dharmasivam@griffith.edu.au](Australia. E-mail: m.dharmasivam@griffith.edu.au); or Dr Des R. Richardson, Centre for Cancer Cell Biology and Drug Discovery, Institute for Biomedicine and Glycomics, Griffith University, Southport, Queensland 4215, Australia. E-mail: [d.richardson@griffith.edu.au](d.richardson@griffith.edu.au)  \n[https://doi.org/10.1016/j.pharmr.2025.100102](https://doi.org/10.1016/j.pharmr.2025.100102)  \n0031-6997/© 2025 American Society for Pharmacology and Experimental Therapeutics. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.  \nD. Emerald Cloud Lab  17  \nE. Wyss Institute “Interrogator”(multi-organ-on-chips)  17  \nF. Atinary and Chemspeed  17  \nG. Autonomous mobile “robo-pharmacologist”  17  \nV. Conclusions and outlook  17  \nA. Global implications  18  \n1. Near-term implications  18  \n2. Long-term implications  18  \nB. Real-world impact and outcomes (2018—2025)  19  \nC. Integration of large language models in drug discovery  19  \nReferences  19  \na r t i c l e i n f o  \nAssociate Editor: Lynette Daws  \na b s t r a c t  \nArtificial intelligence (AI) has progressed from experimental curiosity to clinical utility, with AIdesigned therapeutics now in human trials across diverse therapeutic areas. This review critically","cbCaiuH3ureSSZrR","https://ap.wps.com/l/cbCaiuH3ureSSZrR","pdf",8087584,28,"English","# Abstract\n# Significance Statement\n# I. Introduction\n## A. Exscientia: Generative design and the “Centaur Chemist” approach\n## B. Recursion pharmaceuticals: High-throughput phenotypic discovery\n## C. Insilico Medicine: End-to-end artificial intelligence and generative successes\n## D. BenevolentAI: Knowledge graphs, repurposing, and a cautionary tale\n## E. Schr€odinger: Physics-based simulation meets machine learning\n## F. Comparative summary\n# II. Emerging players and global developments (2024—2025)\n## A. Insitro (USA)\n## B. Isomorphic Labs (UK)\n## C. Atomwise (USA)\n## D. XtalPi and Chinese artificial intelligence biotech companies (China)\n## E. Big pharma’s internal efforts and smaller artificial intelligence startups\n## F. Artificial intelligence in biologics and new therapeutic modalities\n## G. Global and non-Western pharmaceutical artificial intelligence innovation\n# III. Regulatory and ethical considerations in artificial intelligence—driven drug discovery\n## A. Regulatory guidance\n## B. Transparency and explainability\n## C. Data bias and fairness\n## D. Accountability and validation\n## E. Another aspect of accountability is in clinical trials\n## F. Intellectual property and privacy\n# IV. Robotics artificial intelligence in pharmacology: From “Hands-On” to “Self-Driving” discovery\n## A. AstraZeneca iLab (Gothenburg)\n## B. Recursion (Salt Lake City)\n## C. Insitro (South San Francisco)\n## D. Emerald Cloud Lab\n## E. Wyss Institute “Interrogator”(multi-organ-on-chips)\n## F. Atinary and Chemspeed\n## G. Autonomous mobile “robo-pharmacologist”\n# V. Conclusions and outlook\n## A. Global implications\n## B. Real-world impact and outcomes (2018—2025)\n## C. Integration of large language models in drug discovery\n# References","[{\"question\":\"Which five AI-driven drug discovery platforms are compared in the review?\",\"answer\":\"The review compares Exscientia, Recursion Pharmaceuticals, Insilico Medicine, BenevolentAI, and Schrödinger, covering generative design, phenomics-first discovery, end-to-end generative pipelines, knowledge-graph repurposing, and physics-plus machine learning strategies.\"},{\"question\":\"What key post-2024 developments are highlighted for clinical progress?\",\"answer\":\"It highlights positive phase IIa results for Insilico Medicine’s ISM001-055 (Traf2-and Nck-interacting kinase inhibitor) in idiopathic pulmonary fibrosis, and the Recursion–Exscientia merger integrating phenomic screening with automated precision chemistry. It also notes Schrödinger’s physics-enabled strategy reaching late-stage testing via TAK-279 (zasocitinib) into phase III trials.\"},{\"question\":\"How does the review address regulatory, ethical, and data-related risks?\",\"answer\":\"It outlines regulatory guidance and focuses on transparency and explainability, data bias and fairness, accountability and validation (including clinical-trial accountability), and intellectual property and privacy considerations.\"}]","Leading artificial intelligence—driven drug discovery platforms - 2025 landscape and global outlook | PDF",1791154662,71]