Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

• 成果类型: Article
• 署名作者: Koscher, Brent A.; Canty, Richard B.; Mcdonald, Matthew A.; Greenman, Kevin P.; Mcgill, Charles J.; Bilodeau, Camille L.; Jin, Wengong; Wu, Haoyang; Vermeire, Florence H.; Jin, Brooke; Hart, Travis; Kulesza, Timothy; Li, Shih-Cheng; Jaakkola, Tommi S.; Barzilay, Regina; Gomez-Bombarelli, Rafael; Green, William H.; Jensen, Klavs F.
• 署名单位: Massachusetts Institute of Technology (MIT); Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-12064
• DOI: 10.1126/science.adi1407
• 发表日期: 2023-12-22
• 页码: 1374-+
• 关键词: diverse; optimization; solubility; pathways; SYSTEM

摘要

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.