期刊
CHEMICAL COMMUNICATIONS
卷 57, 期 48, 页码 5909-5912出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d1cc00050k
关键词
-
资金
- NIH [P30 CA008748, GM124270]
- Gates Cambridge Trust
The research team used machine learning for novel compound design and synthesis route prediction, successfully identifying chemical scaffolds with antiviral activity. These findings are significant for the development of patent-free antiviral drugs.
The SARS-CoV-2 main viral protease (M-pro) is an attractive target for antivirals given its distinctiveness from host proteases, essentiality in the viral life cycle and conservation across coronaviridae. We launched the COVID Moonshot initiative to rapidly develop patent-free antivirals with open science and open data. Here we report the use of machine learning for de novo design, coupled with synthesis route prediction, in our campaign. We discover novel chemical scaffolds active in biochemical and live virus assays, synthesized with model generated routes.
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