  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 the
alignment of generative models such as large language models and diffusion
models, providing greater flexibility and accuracy by directly aligning model
outputs with human preferences. Building on this advancement, we introduce DPO
to SBDD in this paper. We tailor diffusion models to pharmaceutical needs by
aligning them with elaborately designed chemical score functions. We propose a
new structure-based molecular optimization method called DecompDPO, which
decomposes the molecule into arms and scaffolds and performs preference
optimization at both local substructure and global molecule levels, allowing
for more precise control with fine-grained preferences. 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 in both molecule generation and
optimization, with up to 100% Median High Affinity and a 54.9% Success Rate.
