Journal
MOLECULAR PHARMACEUTICS
Volume 16, Issue 10, Pages 4282-4291Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.molpharmaceut.9b00634
Keywords
structure based drug design; deep learning; generative modeling
Funding
- MINECO (Unidad de Excelencia Maria de Maeztu) [MDM-2014-0370, BIO2017-82628-P]
- FEDER
- European Union [675451]
- Acellera
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Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generative adversarial network to generate, rather than search, diverse three-dimensional ligand shapes complementary to the pocket. Furthermore, we show that the generated molecule shapes can be decoded using a shape-captioning network into a sequence of SMILES enabling directly the structure-based de novo drug design. We evaluate the quality of the method by both structure(docking) and ligand-based [quantitative structure-activity relationship (QSAR)] virtual screening methods. For both evaluation approaches, we observed enrichment compared to random sampling from initial chemical space of ZINC drug-like compounds.
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