Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks.

ABSTRACT: In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists design

SUBMITTER: Segler MHS 

PROVIDER: S-EPMC5785775 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature