Rectified Flow For Structure Based Drug Design

TL;DR

This paper introduces FlowSBDD, a rectified flow-based framework for structure-based drug design that enhances ligand generation quality and diversity, outperforming existing diffusion models without needing specific binding site design.

Contribution

The paper presents a novel rectified flow model framework for drug design, allowing flexible incorporation of additional losses and conditions, improving ligand generation performance.

Findings

Achieves up to -8.50 Avg. Vina Dock score

Attains 75.0% molecular diversity

Outperforms previous diffusion models in ligand quality

Abstract

Deep generative models have achieved tremendous success in structure-based drug design in recent years, especially for generating 3D ligand molecules that bind to specific protein pocket. Notably, diffusion models have transformed ligand generation by providing exceptional quality and creativity. However, traditional diffusion models are restricted by their conventional learning objectives, which limit their broader applicability. In this work, we propose a new framework FlowSBDD, which is based on rectified flow model, allows us to flexibly incorporate additional loss to optimize specific target and introduce additional condition either as an extra input condition or replacing the initial Gaussian distribution. Extensive experiments on CrossDocked2020 show that our approach could achieve state-of-the-art performance on generating high-affinity molecules while maintaining proper molecular properties without specifically designing binding site, with up to -8.50 Avg. Vina Dock score and 75.0% Diversity.