Decomposed Direct Preference Optimization for Structure-Based Drug Design

• URL: http://arxiv.org/abs/2407.13981v2
• Date: Mon, 28 Oct 2024 02:12:08 GMT
• Title: Decomposed Direct Preference Optimization for Structure-Based Drug Design
• Authors: Xiwei Cheng, Xiangxin Zhou, Yuwei Yang, Yu Bao, Quanquan Gu,
• Abstract summary: We propose DecompDPO, a structure-based optimization method to align diffusion models with pharmaceutical needs. DecompDPO can be effectively used for two main purposes: fine-tuning pretrained diffusion models for molecule generation across various protein families, and molecular optimization given a specific protein subpocket after generation.
• Score: 47.561983733291804
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Diffusion models have achieved promising results for Structure-Based Drug Design (SBDD). Nevertheless, high-quality protein subpocket and ligand data are relatively scarce, which hinders the models' generation capabilities. Recently, Direct Preference Optimization (DPO) has emerged as a pivotal tool for aligning generative models with human preferences. In this paper, we propose DecompDPO, a structure-based optimization method aligns diffusion models with pharmaceutical needs using multi-granularity preference pairs. DecompDPO introduces decomposition into the optimization objectives and obtains preference pairs at the molecule or decomposed substructure level based on each objective's decomposability. Additionally, DecompDPO introduces a physics-informed energy term to ensure reasonable molecular conformations in the optimization results. Notably, DecompDPO can be effectively used for two main purposes: (1) fine-tuning pretrained diffusion models for molecule generation across various protein families, and (2) molecular optimization given a specific protein subpocket after generation. Extensive experiments on the CrossDocked2020 benchmark show that DecompDPO significantly improves model performance, achieving up to 95.2% Med. High Affinity and a 36.2% success rate for molecule generation, and 100% Med. High Affinity and a 52.1% success rate for molecular optimization.

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