发表机构
Siebel School of Computing and Data Science; University of Illinois Urbana-Champaign（西贝尔计算与数据科学学院; 伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有药物脱靶结合问题，提出SpecOpt智能体框架，通过残基感知接触差异推理指导大语言模型进行分子修饰，在915个化合物中显著改善靶标-脱靶结合选择性。
AI 中文摘要
脱靶蛋白结合是小分子药物不良反应的主要来源，然而大多数基于结构的分子设计方法侧重于从头生成选择性化合物，而非提高现有、特征明确的药物的选择性。我们引入了特异性优化（SpecOpt），这是一项分子设计任务，旨在对现有化合物进行受限的结构修饰，以增加其对预期靶标相对于已知脱靶蛋白的结合偏好，同时保持其结构同一性和类药性质。为了进行系统评估，我们基于化合物-靶标相互作用数据构建了一个源自 ChEMBL 的基准，通过精选的药物机制注释识别预期靶标，并通过测得的活性识别脱靶蛋白。随后，我们开发了一个智能体框架，该框架将每个化合物与其预期靶标和脱靶蛋白进行对接，通过残基感知的原子-蛋白质接触比较所得构象，并将这些差异相互作用提供给大型语言模型以提出靶向的结构修饰。候选化合物仅在满足分子相似性、ADMET 以及靶标-脱靶对接选择性标准时才会被保留。在 915 个化合物上，该智能体改善了 84.8% 的化合物的靶标-脱靶结合差距，将平均差距从 -0.72 提升至 +0.47 kcal/mol，同时保持与起始化合物的平均 Tanimoto 相似度为 0.72。消融研究确定残基特异性接触信息是关键优化信号：将残基身份替换为二元接触指示符后，所有 29 个消融化合物的改进均消失。这些结果确立了 SpecOpt 作为一个独特的分子设计问题，并证明了残基感知的差异相互作用是提高现有化合物特异性的有效信号。
英文摘要
Off-target protein binding is a major source of adverse effects for small-molecule drugs, yet most structure-based molecular design methods focus on generating selective compounds de novo rather than improving the selectivity of existing, well- characterized drugs. We introduce specificity optimization (SpecOpt), a molecular design task that seeks constrained structural modifications to an existing compound that increase its binding preference for an intended target over known off-targets while preserving its structural identity and drug-like properties. To enable systematic evaluation, we construct a ChEMBL-derived benchmark from compound-target interaction data, identifying intended targets through curated drug-mechanism annotations and off- targets through measured activities. We then develop an agentic framework that docks each compound against its intended target and off-targets, compares the resulting poses through residue-aware atom-protein contacts, and provides these differential interactions to a large language model to propose targeted structural modifications. Candidates are retained only if they satisfy molecular similarity, ADMET, and target-off-target docking selectivity criteria. On 915 compounds, the agent improves the target- off-target binding gap for 84.8% of compounds, shifting the mean gap from -0.72 to +0.47 kcal/mol while maintaining a mean Tanimoto similarity of 0.72 to the starting compounds. Ablation studies identify residue-specific contact information as the critical optimization signal: replacing residue identities with binary contact indicators eliminates improvement on all 29 ablation compounds. These results establish SpecOpt as a distinct molecular design problem and demonstrate residue-aware differential interactions as an effective signal for improving the specificity of existing compounds.
