Rectified Flow For Structure Based Drug Design

Autor:	Zhang, Daiheng, Gong, Chengyue, Liu, Qiang
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Computational Engineering Finance and Science
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to ELLIS ML4Molecules 2024 Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.01174 Zobrazit plný text záznamu View this record from Arxiv