Generating 3D Molecules for Target Protein Binding

TL;DR

This paper introduces GraphBP, a novel machine learning framework that generates 3D molecules capable of binding to specific proteins by sequentially placing atoms using a graph neural network and flow models, ensuring geometry and chemical accuracy.

Contribution

The paper presents a new method combining 3D graph neural networks and flow models for sequential molecule generation targeting protein binding sites, with an emphasis on equivariance and dependency modeling.

Findings

GraphBP effectively generates 3D molecules with binding ability.

The method preserves geometric equivariance during molecule generation.

Experimental results demonstrate the approach's success in binding site targeting.

Abstract

A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and effective framework, known as GraphBP, to generate 3D molecules that bind to given proteins by placing atoms of specific types and locations to the given binding site one by one. In particular, at each step, we first employ a 3D graph neural network to obtain geometry-aware and chemically informative representations from the intermediate contextual information. Such context includes the given binding site and atoms placed in the previous steps. Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system. Finally, to place a new atom, we generate its atom type and relative location w.r.t. the constructed local coordinate system via a flow model. We also consider generating the variables of interest sequentially to capture the underlying dependencies among them. Experiments demonstrate that our GraphBP is effective to generate 3D molecules with binding ability to target protein binding sites. Our implementation is available at https://github.com/divelab/GraphBP.