Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

Yulun Wu, Nicholas Choma, Andrew Deru Chen, Mikaela Cashman, Erica Teixeira Prates, Veronica G Melesse Vergara, Manesh B Shah, Austin Clyde, Thomas Brettin, Wibe Albert de Jong, Neeraj Kumar, Martha S Head, Rick L. Stevens, Peter Nugent, Daniel A Jacobson, James B Brown

Keywords: reinforcement learning, graph neural network, molecule generation, drug discovery, curiosity-driven policy

Abstract: We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

One-sentence Summary: We developed a reinforcement learning framework that advances in exploiting spatial and attributional molecular information as well as exploring novel and synthesizable chemical structures for the purpose of antiviral drug discovery.