CrossDocked2020

In structure-based drug design (SBDD), a major challenge is generating high-affinity 3D ligand molecules that can effectively bind to specific protein targets, which requires accurately capturing complex protein-ligand interactions. Although existing diffusion models have demonstrated potential in molecular generation tasks, they often struggle with accurately capturing the complex interactions between proteins and ligands. To address this problem, we propose MSIDiff, a multi-stage interaction-aware diffusion model for protein-specific molecular generation. MSIDiff uses the pre-trained model MSINet to extract real protein-ligand interaction information during the initial diffusion stage and incorporates this information into the reverse process to ensure that the generated molecules exhibit accurate interaction relationships with target proteins. Through a scoring mechanism, MSIDiff filters key nodes to extract crucial protein-ligand interaction data and uses the cross-layer interaction update module with GRU to recursively integrate information from different denoising stages, enabling effective cross-layer information transmission. Experimental results on the CrossDocked2020 dataset show that MSIDiff can generate molecules with more realistic 3D structures and higher binding affinity to protein targets, achieving an Avg. Vina Score of up to -6.36, while maintaining appropriate molecular properties.

在基于结构的药物设计（SBDD）领域，一项主要挑战是生成具有高亲和力的三维配体分子，这些分子能够有效结合特定的蛋白质靶点，这要求精确捕捉复杂的蛋白质-配体相互作用。尽管现有的扩散模型在分子生成任务中已显示出潜在的应用价值，但它们往往难以精确捕捉蛋白质与配体之间的复杂相互作用。为解决此问题，我们提出了MSIDiff，这是一种多阶段交互感知扩散模型，用于针对蛋白质的分子生成。MSIDiff利用预训练模型MSINet在初始扩散阶段提取真实的蛋白质-配体相互作用信息，并将这些信息整合到逆向过程中，以确保生成的分子与目标蛋白质展现出精确的相互作用关系。通过评分机制，MSIDiff筛选关键节点以提取关键的蛋白质-配体相互作用数据，并使用带有GRU的跨层交互更新模块递归地整合来自不同去噪阶段的信息，从而实现有效的跨层信息传递。在CrossDocked2020数据集上的实验结果表明，MSIDiff能够生成具有更真实三维结构和更高结合蛋白质靶点亲和力的分子，平均Vina评分高达-6.36，同时保持适当的分子特性。

搜集汇总

背景与挑战

背景概述

CrossDocked2020是一个用于结构基药物设计（SBDD）的基准数据集，专注于蛋白质-配体相互作用研究，旨在生成高亲和力的3D配体分子。该数据集属于生物医学与健康科学类别，支持多种文件格式，但当前文件尚未上传，且需要IEEE DataPort订阅才能访问。