Generating ligand molecules for specific protein targets, known as
structure-based drug design, is a fundamental problem in therapeutics
development and biological discovery. Recently, target-aware generative models,
especially diffusion models, have shown great promise in modeling
protein-ligand interactions and generating candidate drugs. However, existing
models primarily focus on learning the chemical distribution of all drug
candidates, which lacks effective steerability on the chemical quality of model
generations. In this paper, we propose a novel and general alignment framework
to align pretrained target diffusion models with preferred functional
properties, named AliDiff. AliDiff shifts the target-conditioned chemical
distribution towards regions with higher binding affinity and structural
rationality, specified by user-defined reward functions, via the preference
optimization approach. To avoid the overfitting problem in common preference
optimization objectives, we further develop an improved Exact Energy Preference
Optimization method to yield an exact and efficient alignment of the diffusion
models, and provide the closed-form expression for the converged distribution.
Empirical studies on the CrossDocked2020 benchmark show that AliDiff can
generate molecules with state-of-the-art binding energies with up to -7.07 Avg.
Vina Score, while maintaining strong molecular properties.

提出了一种名为 AliDiff 的新型通用对齐框架，该框架可以将预训练的目标扩散模型与所需的功能属性对齐，以优化化学物质的生成。经过实证研究，发现 AliDiff 可以生成具有最先进的结合能的分子，并且仍然具有强大的分子特性。