{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:36:26Z","timestamp":1792092986025,"version":"4.4.0"},"reference-count":96,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:p>Artificial intelligence is quietly reconfiguring the landscape of drug discovery. It sharpens the search for new targets, refines lead compounds, and helps tailor clinical trial designs. Behind these advances sits a family of methods\u2014convolutional, recurrent, and graph neural networks, generative adversarial networks, variational autoencoders, diffusion models, and Transformers\u2014that has found its way into every stage of the pipeline. Tasks that once demanded years of trial and error, from pulling meaningful features out of molecules to predicting drug-target affinity and crystal structures with polymorph stability, now run faster and often with greater accuracy. This early insight lets researchers flag solid-form properties that influence bioavailability and manufacturability, trimming timelines and, in principle, lifting success rates. To map a field that keeps shifting shape, we searched PubMed, Web of Science, Scopus, and Google Scholar for peer-reviewed reports published between January 2007 and April 2026. The story the clinical cases tell is double-edged: one AI-discovered candidate has reached Phase IIa with encouraging efficacy, another stalled in Phase I when safety signals surfaced. AI can catch adverse effects earlier in toxicity assessments, yet nagging hurdles endure\u2014biological complexity, patchy data, and a shortage of scientists fluent in both machine learning and pharmaceutics. Digging ourselves out will demand data standardization, nimble regulatory frameworks, and cross-disciplinary training. Weaving multimodal data together with explainable AI is becoming non-negotiable for transparency and regulatory confidence. The technology is now stretching into complex systems like Traditional Chinese Medicine and natural product screening. As AI continues to mature, it will reshape drug development in ways we are only beginning to grasp, but one truth remains stubborn: clinical translation still rests on rigorous experimental evidence.<\/jats:p>","DOI":"10.3389\/fphar.2026.1870527","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T04:23:13Z","timestamp":1783052593000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation"],"prefix":"10.3389","volume":"17","author":[{"given":"Zihan","family":"Mao","sequence":"first","affiliation":[{"name":"Department of Colorectal Surgery, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihui","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Pain Relief Therapeutic, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"10347","DOI":"10.1007\/s11042-024-19361-y","article-title":"Understanding GANs: fundamentals, variants, training challenges, applications, and open problems","volume":"84","author":"Ahmad","year":"2024","journal-title":"Multimed. 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