SurfSpec：通过结合口袋-配体几何不匹配边界增强非靶标无关的特异性
SurfSpec: Enhancing Off-Target-Agnostic Specificity by Bounding Pocket-Ligand Geometric Mismatch
- KAIST（韩国科学技术院）
- Seoul National University（首尔国立大学）
- Calici
- AITRICS
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SurfSpec是一种无需非靶标结构先验知识的先导优化框架，通过结合口袋-配体几何不匹配边界提升非靶标无关特异性，在CrossDocked2020测试集上表现优于基线。
AI中文摘要：
基于结构的药物设计中的先导优化旨在提高靶标结合能力，同时避免与非靶标结合口袋发生意外相互作用。然而，现有的亲和力驱动方法并未明确控制特异性，而当前的特异性感知方法通常需要非靶标结构的先验知识。我们通过分析配体与靶标结合口袋之间的几何不匹配，解决非靶标无关的特异性感知先导优化问题。我们为几何上分离的非靶标提供保守的特异性下界，无需获取非靶标结构。通过将结合口袋-配体不匹配度量化，三角不等式表明，减少靶标-配体不匹配可提高分离非靶标类别的不匹配保守下界，该下界可通过经验几何-亲和力校准转化为特异性下界。受此分析启发，我们引入SurfSpec，一种非靶标无关的先导优化框架，该框架迭代地使配体向靶标结合口袋表面的未充分占据区域生长。SurfSpec在所选靶标表面补丁的连接基生成（提供几何伪标签）和在结合口袋条件配体先验下的细化（将这些伪标签恢复为有效配体）之间交替进行。在CrossDocked2020测试集上，SurfSpec减少了几何不匹配，在经验特异性方面优于所评估的非靶标无关先导优化基线，同时保持了具有竞争力的靶标亲和力提升。
英文摘要：
Lead optimization in structure-based drug design aims to improve target binding while avoiding unintended interactions with off-target pockets. However, existing affinity-driven methods do not explicitly control specificity, whereas current specificity-aware approaches commonly require prior knowledge of off-target structures. We address off-target-agnostic specificity-aware lead optimization by analyzing the geometric mismatch between a ligand and the target pocket. We provide a conservative specificity lower bound for geometrically separated off-targets without requiring access to off-target structures. By metricizing pocket--ligand mismatch, the triangle inequality shows that reducing target--ligand mismatch improves a conservative lower bound on mismatch to a separated off-target class, which can be translated into a specificity lower bound through an empirical geometry--affinity calibration. Motivated by this analysis, we introduce SurfSpec, an off-target-agnostic lead optimization framework that iteratively grows ligands toward under-occupied regions of the target pocket surface. SurfSpec alternates between linker generation toward selected target-surface patches, which provides geometric pseudo-labels, and refinement under a pocket-conditioned ligand prior, which restores these pseudo-labels into valid ligands. On the CrossDocked2020 test set, SurfSpec reduces geometric mismatch and outperforms evaluated off-target-agnostic lead optimization baselines in empirical specificity, while maintaining competitive target-affinity improvement.
