Molecule Generation for Target Receptor Binding via Continuous Normalizing Flows

Eyal Rozenberg, Daniel Freedman

Keywords: molecule generative models, normalizing flows, equivariance

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. The distribution should be invariant to rigid body transformations that act 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, attaining a 52.7% relative improvement over the current state of the art.

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TL;DR: We derive semi-equivariance conditions to yield invariant conditional distributions of ligand molecules given receptor molecules.