Title: Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug
Authors: Yulun Wu, Nicholas Choma, Andrew Chen, Mikaela Cashman, \'Erica T.
Prates, Manesh Shah, Ver\'onica G. Melesse Vergara, Austin Clyde, Thomas S.
Brettin, Wibe A. de Jong, Neeraj Kumar, Martha S. Head, Rick L. Stevens,
Peter Nugent, Daniel A. Jacobson, James B. Brown
Abstract: We developed Distilled Graph Attention Policy Networks (DGAPNs), a
curiosity-driven 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 Network (sGAT) that leverages self-attention over both node and
edge attributes as well as encoding spatial structure -- this capability is of
considerable interest in areas such as molecular and synthetic biology and drug
discovery. An attentional policy network is then introduced to learn decision
rules for a dynamic, fragment-based chemical environment, and state-of-the-art
policy gradient techniques are employed to train the network with enhanced
stability. Exploration is efficiently encouraged by incorporating 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 increasing the diversity of proposed
molecules and reducing the complexity of paths to chemical synthesis.