Structure-Based Drug Design via Semi-Equivariant Conditional Normalizing Flows

Eyal Rozenberg, Ehud Rivlin, Daniel Freedman

Keywords: molecule generative models, structural biology, drug design, normalizing flows, equivariance, 3D graphs conditional distributions

TL;DR: We propose an algorithm for learning a conditional generative model of a molecule given a target, based on a continuous normalizing flow. The algorithm is formulated mathematically as learning conditional distributions between two 3D graphs.

Abstract: We propose an algorithm for learning a conditional generative model of a molecule given a target. Specifically, given a receptor molecule that one wishes to bind to, the conditional model generates candidate ligand molecules that may bind to it. Our problem is formulated mathematically as learning conditional distributions between two 3D graphs. The distribution should be invariant to rigid body transformations that act $\textit{jointly}$ on the ligand and the receptor; it should also be invariant to permutations of either the ligand or receptor atoms. Our learning algorithm is based on a continuous normalizing flow. We establish semi-equivariance conditions on the flow which guarantee the aforementioned invariance conditions on the conditional distribution. We propose a graph neural network architecture which implements this flow, and which is designed to learn effectively despite the vast differences in size between the ligand and receptor. We evaluate our method on the CrossDocked2020 dataset, displaying high quality performance in the key $\Delta$Binding metric. We also demonstrate how the learned density may be usefully employed to define a scoring function.