{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T18:53:13Z","timestamp":1781895193365,"version":"3.54.5"},"reference-count":38,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"25","content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Adv."],"published-print":{"date-parts":[[2026,6,19]]},"abstract":"<jats:p>\n                    This study introduces a unified framework combining artificial intelligence (AI)\u2013directed de novo molecular generation with dual-track lead optimization\u2014comprising expert-guided strategies and AI-driven pathways\u2014to discover tyrosinase (TYR) inhibitors for hyperpigmentation disorders. Using a reinforcement learning (RL)\u2013based generative model, the lead compound\n                    <jats:bold>AI10<\/jats:bold>\n                    was identified. Subsequent optimization followed two parallel routes. The expert-guided approach yielded\n                    <jats:bold>AI10-m15<\/jats:bold>\n                    as the most potent TYR inhibitor, with notable antipigmentation activity and excellent cellular safety profiles. In contrast, the AI-driven pathway explored broader chemical spaces, generating unconventional chemotypes, exemplified by the potent TYR inhibitor\n                    <jats:bold>AI10-a2<\/jats:bold>\n                    , highlighting AI\u2019s capacity to uncover nonintuitive activity cliffs despite greater output variability. Systematic comparison revealed that the AI model offers exploratory diversity, whereas expert-guided optimization provides predictable improvements in activity and developability. In summary, starting from an AI-generated lead and subsequently integrating both expert-guided and AI-driven structural optimization strategies, these findings further underscore that combining AI technologies with experts\u2019 medicinal chemistry insights can substantially accelerate the discovery of viable candidate compounds.\n                  <\/jats:p>","DOI":"10.1126\/sciadv.aeg0376","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T18:00:33Z","timestamp":1781892033000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Discovery of TYR inhibitors from de novo molecular generation to dual-track lead optimization: \u201cCompetition\u201d between AI and chemists"],"prefix":"10.1126","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9519-1822","authenticated-orcid":true,"given":"Yinyan","family":"Sun","sequence":"first","affiliation":[{"name":"School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China."},{"name":"Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; 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