Rectified Flow For Structure Based Drug Design
This work addresses structure-based drug design for pharmaceutical applications, offering an incremental improvement over existing diffusion models.
The authors tackled the problem of generating 3D ligand molecules for drug design by proposing FlowSBDD, a rectified flow-based framework that incorporates additional losses and conditions, achieving state-of-the-art performance with up to -8.50 Avg. Vina Dock score and 75.0% diversity on CrossDocked2020.
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.