发表机构
North Carolina State University（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出后训练框架VINCENT，通过闭环扰动验证生成满足化学连贯等标准的跨药物基序对协同作用解释，其基序召回率和真阳性/真阴性分离性能优于基线模型。
AI 中文摘要
药物协同作用预测用于评估两种药物是否会产生比各自活性预期更强的联合效应。在药物组合发现中，单一协同作用评分通常不足：研究人员还需要知道哪些分子区域共同驱动了预测结果。本研究聚焦于基序对协同作用解释，即识别化学上连贯的区域对，每种药物各贡献一个，它们共同促成了预测的协同作用。现有可解释的协同作用模型会暴露原子或亚结构层面的信号，但这些解释嵌入在预测器架构中，且没有一种模型能在重复扰动下验证跨药物区域评分，或将该证据反馈以优化解释。可靠的基序对解释应满足化学连贯性、扰动稳定性以及与预测器行为一致这三个标准。我们提出VINCENT（Validated Interaction Network for Cross-drug Explanation of Therapeutics），这是一个针对固定的感知相互作用的协同作用预测器的后训练框架。VINCENT从注意力和梯度信号中提取原子对证据，将原子分组为化学上连贯的基序，并通过重复局部扰动验证候选基序对。经验证的证据会被反馈以优化基序分配，从而产生满足上述三个标准的解释。在一个包含25对经文献注释的子集上，VINCENT的平均基序召回率为0.826（95%置信区间：0.78-0.87），而基线模型的召回率为0.49-0.66；在全部71个测试对上，其经验证的相互作用评分实现了3.36的真阳性/真阴性分离。这些结果表明，闭环扰动验证相比现有方法能更准确地恢复文献支持的分子区域，同时生成更能反映预测器行为的跨药物相互作用评分。
英文摘要
Drug synergy prediction estimates whether two drugs produce a stronger joint effect than expected from their individual activities. For drug combination discovery, a single synergy score is often not enough: researchers also need to know which molecular regions jointly drive the prediction. We study motif-pair synergy explanation, which identifies pairs of chemically coherent regions, one from each drug, that jointly contribute to predicted synergy. Existing interpretable synergy models expose atom- or substructure-level signals, but their explanations are built into the predictor architecture, and none validates cross-drug region scores under repeated perturbations or feeds that evidence back to refine the explanation. A reliable motif-pair explanation should instead be chemically coherent, perturbation-stable, and aligned with predictor behavior. We introduce VINCENT (Validated Interaction Network for Cross-drug Explanation of Therapeutics), a post-training framework for a fixed interaction-aware synergy predictor. VINCENT extracts atom-pair evidence from attention and gradient signals, groups atoms into chemically coherent motifs, and validates candidate motif pairs through repeated local perturbations. The validated evidence is fed back to refine motif assignments, yielding explanations that satisfy these three criteria. On a 25-pair literature-annotated subset, VINCENT achieves a mean motif recall of 0.826 (95% CI: 0.78-0.87), compared with 0.49-0.66 for baselines. Across all 71 test pairs, its validated interaction scores yield a TP/TN separation of 3.36. These results show that closed-loop perturbation validation recovers literature-supported molecular regions more accurately than existing alternatives while producing cross-drug interaction scores that better reflect predictor behavior.
Comments18 pages, 4 figures
