Semi-Equivariant Continuous Normalizing Flows for Target-Aware Molecule Generation
This work addresses the challenge of target-aware molecule generation for drug discovery, representing an incremental advance with a novel method for a known bottleneck.
The paper tackles the problem of generating ligand molecules that bind to a given receptor by proposing a semi-equivariant continuous normalizing flow model, which achieves a significant improvement in binding affinity on the CrossDocked2020 dataset compared to competing methods.
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 $\textit{jointly}$ 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, attaining a significant improvement in binding affinity over competing methods.