Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development. There is a growing need for de-novo design methods that would address this problem. We present two complementary approaches: ReLeaSE, a sequence SMILES based and MolecularRNN, the graph recurrent generative model for molecular structures. Our model generates diverse realistic molecular graphs after likelihood pretraining on a big database of molecules. Further, the model is tuned with a multiobjective policy gradient algorithm, provided a critic that estimates the reward for the multiple properties of interest. We show a significant distribution shift to the desired range for biological activity, physical, and ADMET properties outperforming state-of-the-art works. Finally, we show experimental validation of our computational hits on the example of the design of selective and dual-kinase inhibitors.

Accelerating hit discovery with multiobjective deep reinforcement learning

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.