提出可解释的活性悬崖预测方法，显著减少药物化学家的实验探索量。
Predicting Activity Cliffs for Autonomous Medicinal Chemistry
- 基于2500万分子对，用简单规则识别易变位置，无需机器学习。
- 11特征模型在六类蛋白上准确预测真正活性悬崖，提升2倍命中率。
- 工具开源并提供网页交互界面，适合药物发现研发人员使用。
活性悬崖预测——即识别结构微小变化导致活性剧烈波动的位置——一直是计算药物化学中的难题。本研究采用简洁定义：哪些位置的小幅修改最可能引发活性变化。基于50个ChEMBL靶标、六类蛋白家族的2500万对匹配分子对，计算位置级敏感性发现，两个问题本质不同：‘哪些位置变异最大？’仅由骨架大小决定（NDCG@3 = 0.966），无需机器学习；而‘哪些是真正的活性悬崖？’（通过SALI归一化捕捉小改大效）则需11特征模型结合3D药效团上下文（NDCG@3 = 0.910 vs. 随机0.839），跨六类蛋白、新骨架（0.913）和时间分割（0.878）均表现良好。该模型首次识别悬崖位点的成功率达53%（随机27%，提升2倍），将化学家首轮需探索位置从3.1个降至2.1个，减少31%实验量。仅凭结构预测具体修饰不可行（斯皮尔曼相关0.268，新骨架下降为-0.31）。系统已开源代码并提供交互式网页应用。
原文摘要 · Abstract (English)
Activity cliff prediction - identifying positions where small structural changes cause large potency shifts - has been a persistent challenge in computational medicinal chemistry. This work focuses on a parsimonious definition: which small modifications, at which positions, confer the highest probability of an outcome change. Position-level sensitivity is calculated using 25 million matched molecular pairs from 50 ChEMBL targets across six protein families, revealing that two questions have fundamentally different answers. "Which positions vary most?" is answered by scaffold size alone (NDCG@3 = 0.966), requiring no machine learning. "Which are true activity cliffs?" - where small modifications cause disproportionately large effects, as captured by SALI normalization - requires an 11-feature model with 3D pharmacophore context (NDCG@3 = 0.910 vs. 0.839 random), generalizing across all six protein families, novel scaffolds (0.913), and temporal splits (0.878). The model identifies the cliff-prone position first 53% of the time (vs. 27% random - 2x lift), reducing positions a chemist must explore from 3.1 to 2.1 - a 31% reduction in first-round experiments. Predicting which modification to make is not tractable from structure alone (Spearman 0.268, collapsing to -0.31 on novel scaffolds). The system is released as open-source code and an interactive webapp.
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