DecompDiff：具有分解先验的扩散模型用于基于结构的药物设计
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
AI总结：
针对现有药物设计方法忽略配体原子角色差异的问题，提出DecompDiff扩散模型，通过分解臂和骨架先验并引入键扩散与有效性引导，在CrossDocked2020上实现高亲和力分子生成，达到最优性能。
摘要：
在目标结合位点内设计3D配体是药物发现中的一项基本任务。现有的基于结构的药物设计方法将所有配体原子同等对待，这忽略了原子在配体中对于药物设计的不同作用，并且可能在探索大型类药物分子空间时效率较低。在本文中，受制药实践中惯例的启发，我们将配体分子分解为两部分，即臂和骨架，并提出一种新的扩散模型DecompDiff，该模型在臂和骨架上具有分解先验。为了促进分解生成并改善生成分子的性质，我们在模型中引入了键扩散，并在采样阶段加入了额外的有效性引导。在CrossDocked2020上的大量实验表明，我们的方法在生成高亲和力分子方面达到了最先进的性能，同时保持了适当的分子性质和构象稳定性，平均Vina Dock得分为-8.39，成功率为24.5%。代码可在https://github.com/bytedance/DecompDiff获取。
英文摘要：
Designing 3D ligands within a target binding site is a fundamental task in drug discovery. Existing structured-based drug design methods treat all ligand atoms equally, which ignores different roles of atoms in the ligand for drug design and can be less efficient for exploring the large drug-like molecule space. In this paper, inspired by the convention in pharmaceutical practice, we decompose the ligand molecule into two parts, namely arms and scaffold, and propose a new diffusion model, DecompDiff, with decomposed priors over arms and scaffold. In order to facilitate the decomposed generation and improve the properties of the generated molecules, we incorporate both bond diffusion in the model and additional validity guidance in the sampling phase. Extensive experiments on CrossDocked2020 show that our approach achieves state-of-the-art performance in generating high-affinity molecules while maintaining proper molecular properties and conformational stability, with up to -8.39 Avg. Vina Dock score and 24.5 Success Rate. The code is provided at https://github.com/bytedance/DecompDiff
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
