Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks

TL;DR

This paper introduces MolJO, a gradient-guided Bayesian flow network framework for structure-based molecule optimization that achieves state-of-the-art results and versatility in drug design tasks.

Contribution

The paper presents MolJO, a novel gradient-guided Bayesian inference framework for SBMO that effectively guides discrete and continuous data while maintaining SE(3)-equivariance.

Findings

State-of-the-art success rate of 51.3% on CrossDocked2020

More than 4x improvement over previous gradient-based methods

Effective in multi-objective and complex drug design tasks

Abstract

Structure-Based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO), the gradient-based SBMO framework that facilitates joint guidance signals across different modalities while preserving SE(3)-equivariance. We introduce a novel backward correction strategy that optimizes within a sliding window of the past histories, allowing for a seamless trade-off between explore-and-exploit during optimization. MolJO achieves state-of-the-art performance on CrossDocked2020 benchmark (Success Rate 51.3%, Vina Dock -9.05 and SA 0.78), more than 4x improvement in Success Rate compared to the gradient-based counterpart, and 2x "Me-Better" Ratio as much as 3D baselines. Furthermore, we extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug design such as R-group optimization and scaffold hopping, further underscoring its versatility. Code is available at https://github.com/AlgoMole/MolCRAFT.