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Mol Pharm. 2019 Oct 7;16(10):4282-4291. doi: 10.1021/acs.molpharmaceut.9b00634. Epub 2019 Sep 10.

From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Author information

1
Computational Science Laboratory , Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) , C Dr Aiguader 88 , 08003 Barcelona , Spain.
2
Biogen Chemistry and Molecular Therapeutics , 115 Broadway Street , Cambridge , Massachusetts 02142 , United States.
3
Acellera, Barcelona Biomedical Research Park (PRBB) , C Dr. Aiguader 88 , 08003 Barcelona , Spain.
4
Instituci├│ Catalana de Recerca i Estudis Avan├žats (ICREA) , Passeig Lluis Companys 23 , 08010 Barcelona , Spain.

Abstract

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.

KEYWORDS:

deep learning; generative modeling; structure based drug design

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