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Graph Networks for Molecular Design

preprint
submitted on 21.08.2020 and posted on 24.08.2020 by Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, Esben Jannik Bjerrum
Deep learning methods applied to chemistry can be used to accelerate the discovery of new molecules. This work introduces GraphINVENT, a platform developed for graph-based molecular design using graph neural networks (GNNs). GraphINVENT uses a tiered deep neural network architecture to probabilistically generate new molecules a single bond at a time. All models implemented in GraphINVENT can quickly learn to build molecules resembling the training set molecules without any explicit programming of chemical rules. The models have been benchmarked using the MOSES distribution-based metrics, showing how GraphINVENT models compare well with state-of-the-art generative models. This work is one of the first thorough graph-based molecular design studies, and illustrates how GNN-based models are promising tools for molecular discovery.

History

Email Address of Submitting Author

rocio.mercado@astrazeneca.com

Institution

AstraZeneca

Country

Sweden

ORCID For Submitting Author

0000-0002-6170-6088

Declaration of Conflict of Interest

The authors declare no competing financial interests.

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