Theoretical and Computational Chemistry

De novo drug design using reinforcement learning with graph-based deep generative models



Machine learning methods have proven to be effective tools for molecular design, allowing for efficient exploration of the vast chemical space via deep molecular generative models. Here, we propose a graph-based deep generative model for de novo molecular design using reinforcement learning. We demonstrate how the reinforcement learning framework can successfully fine-tune the generative model towards molecules with various desired sets of properties, even when few molecules have the goal attributes initially. We explored the following tasks: decreasing/increasing the size of generated molecules, increasing their drug-likeness, and increasing protein-binding activity. Using our model, we are able to generate 95% predicted active compounds for a common benchmarking task, outperforming previously reported methods on this metric.


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Supplementary weblinks

This is the source code for RL-GraphINVENT, a platform for graph-based targeted molecular generation using graph neural networks and reinforcement learning. RL-GraphINVENT uses a Gated Graph Neural Network -based model fine-tuned using reinforcement learning to probabilistically generate new molecules with desired property profiles.