发表机构
University of Southern California; University of California, Berkeley（南加州大学; 加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对蛋白质-配体结合亲和力预测中引擎结果不一致问题，提出RELIABLE-BA框架，通过三步实现多引擎预测，经实验验证其能提升预测竞争力和不确定性校准，可筛选高置信对减少误差，是首个结合证据融合与上下文可靠性的预测框架。
AI 中文摘要
准确的蛋白质-配体结合亲和力预测是计算药物发现的核心，但现代对接引擎常常给出不一致的结果且无置信度指示。共识评分和集成方法虽提高了平均准确率，但未考虑化学背景。为此提出RELIABLE-BA框架，通过三步实现多引擎结合亲和力预测：将各引擎建模为证据专家，根据分子背景缩放认知不确定性并保留预测均值，通过闭式聚合融合专家。实验表明该方法有竞争力且不确定性校准显著改善，能可靠筛选蛋白质-配体对，减少预测误差。这也是首个将证据融合与上下文相关可靠性结合的多引擎预测框架，为可信AI指导药物发现提供了原则性路径。
英文摘要
Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust. Consensus scoring and ensemble methods improve mean accuracy but treat all predictions identically without interpretable confidence measures or uncertainty decomposition, ignoring the chemical context of each protein-ligand pair. To address this limitation, we introduce RELIABLE-BA (RELIABiLity-aware Evidential fusion for Binding Affinity), an evidential framework for multi-engine binding affinity prediction. Our model comprises three steps: (1) modeling each engine as an evidential expert via Normal-Inverse-Gamma distributions, (2) scaling epistemic uncertainty through learned reliability from molecular context while preserving each expert's predictive mean, and (3) fusing experts through closed-form aggregation that captures both individual uncertainty and inter-engine disagreement. Experiments on the PDBBind and BDB2020+ benchmarks demonstrate competitive point prediction with substantially improved uncertainty calibration, and additional validation on the SARS-CoV-2 Mpro dataset and 5HT2A receptor demonstrates applicability to clinically relevant drug targets. Crucially, these uncertainty estimates enable reliable filtering of protein-ligand pairs, reducing prediction error by up to 25% when retaining only high-confidence pairs. To our knowledge, RELIABLE-BA is the first multi-engine binding affinity prediction framework to combine evidential fusion with context-dependent reliability, offering a principled path toward trustworthy AI-guided drug discovery. Our code is publicly available at https://github.com/yongchand/RELIABLE-BA.
Journal refKDD '2026: Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
