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. Code is available at https://github.com/laviaf/DecompDPO.

翻译：扩散模型在基于结构的药物设计领域已取得显著成果。然而，高质量蛋白质亚口袋与配体数据相对稀缺，制约了模型的生成能力。近期，直接偏好优化已成为使生成模型与人类偏好对齐的关键工具。本文提出DecompDPO——一种基于结构的优化方法，通过多粒度偏好配对使扩散模型与药物研发需求对齐。该方法将分解机制引入优化目标，根据各目标的可分解性在分子或分解子结构层面构建偏好对。此外，DecompDPO引入物理信息能量项以确保优化结果中分子构象的合理性。值得注意的是，该方法可有效应用于两大场景：(1)针对不同蛋白质家族进行分子生成的预训练扩散模型微调；(2)在特定蛋白质亚口袋条件下对生成分子进行优化。在CrossDocked2020基准测试上的大量实验表明，DecompDPO显著提升了模型性能：在分子生成任务中达到95.2%的中高亲和力与36.2%的成功率，在分子优化任务中实现100%的中高亲和力与52.1%的成功率。代码已开源：https://github.com/laviaf/DecompDPO。