Abstract

Generating 3D molecules that bind to specific protein targets via generative models has shown great promise in structure-based drug design. Recently, diffusion-based methods have achieved promising results, but their reliance on high sampling steps poses risks of slowing the drug discovery process due to increased time and computational costs. In this work, we propose a novel method named Perturbed Flow Matching (PFM), which significantly reduces sampling steps by leveraging a Flow Matching framework. PFM introduces a unique perturbed conditional probability path design that incorporates pocket binding site information and atom type-coordinate coupled information to enhance molecular generation performance. Experiments on CrossDocked2020 dataset demonstrate that PFM generates molecules with competitive 3D structures and state-of-the-art (SOTA) binding affinities towards the protein targets, achieving an Avg. of -7.12. Additionally, PFM accelerates the generation of valid molecules by a factor of 21.3, while demonstrating potential for further improvement. The code is available at https://github.com/kurisu92725/PFM.