READ：一种用于基于结构的药物设计的检索-对齐扩散框架
READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design
AI总结：
针对现有SBDD模型忽略蛋白-配体复合物相似性及数据有限的问题，提出READ检索-对齐扩散框架，在标准化对接评估中表现优于现有方法，为早期药物计算命中物生成提供实用框架。
摘要：
基于结构的药物设计（SBDD）模型是现代药物研发的核心，可实现原子级分辨率下蛋白-配体相互作用的合理探索。然而，现有多数方法将分子生成视为孤立优化或一对一匹配任务，忽略了蛋白-配体复合物间的共享结合模式与内在相似性，这种碎片化视角限制了其捕捉分子识别及结合特异性基本原理的能力；此外，高质量实验数据的有限可用性进一步阻碍了模型的泛化性与实际应用。为应对这些挑战，本文提出READ，一种检索-对齐分子生成框架，其生成过程以靶向同源蛋白的小分子为条件；检索得到的配体在多个表示空间中与扩散模型对齐，并在生成的各阶段作为条件引导整合。在标准化的基于对接的评估协议下，READ相较于现有SBDD方法表现始终强劲；更重要的是，其为基于结构的分子生成引入了检索-对齐范式，为早期计算命中物生成提供了实用框架，而前瞻性实验验证留待后续工作开展。
英文摘要：
Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution. However, most existing approaches frame molecular generation as an isolated optimization or a one-to-one matching task, overlooking the shared binding patterns and intrinsic similarities among protein-ligand complexes. This fragmented perspective constrains their ability to capture the fundamental principles governing molecular recognition and binding specificity. Moreover, the limited availability of high-quality experimental data further hampers model generalization and real-world applicability. To address these challenges, we present READ, a retrieval-alignment molecular generation framework that conditions the generative process on small molecules targeting homologous proteins. Retrieved ligands are aligned with a diffusion model across multiple representational spaces and integrated as conditional guidance throughout successive stages of generation. Under a standardized docking-based evaluation protocol, READ achieves consistently strong performance against state-of-the-art SBDD methods. More importantly, it introduces a retrieval-alignment paradigm for structure-based molecular generation, offering a practical framework for early-stage computational hit generation while leaving prospective experimental validation as future work.
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。
