期刊
COMMUNICATIONS CHEMISTRY
卷 1, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s42004-018-0068-1
关键词
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资金
- Swiss National Science Foundation [IZSEZ0_177477]
- ETH Zurich Postdoctoral Fellowship [16-2 FEL-07]
- Swiss National Science Foundation (SNF) [IZSEZ0_177477] Funding Source: Swiss National Science Foundation (SNF)
Instances of artificial intelligence equip medicinal chemistry with innovative tools for molecular design and lead discovery. Here we describe a deep recurrent neural network for de novo design of new chemical entities that are inspired by pharmacologically active natural products. Natural product characteristics are incorporated into a deep neural network that has been trained on synthetic low molecular weight compounds. This machine-learning model successfully generates readily synthesizable mimetics of the natural product templates. Synthesis and in vitro pharmacological characterization of four de novo designed mimetics of retinoid X receptor modulating natural products confirms isofunctional activity of two computer-generated molecules. These results positively advocate generative neural networks for natural-product-inspired drug discovery, reveal both opportunities and certain limitations of the current approach, and point to potential future developments.
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