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MAPCliff-WMGR:探索分子活动预测中的活动悬崖,通过加权分子图表表示增强分子活动预测

Yiwei Chen¹, Tingfang Wu^1,2, Yelu Jiang¹

¹School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.

Journal of chemical information and modeling

|November 18, 2025

概括

预测分子活动是药物发现的关键. 新的计算框架MAPCliff-WMGR准确地识别了活动悬崖,改善了药物查和开发.

科学领域:

计算化学是一种计算化学.
药品化学药品化学是一个
药物发现药物发现

背景情况:

准确的分子活动预测对于药物发现至关重要.
活动悬崖,类似分子具有不同的活动,构成一个重大挑战.

研究的目的:

介绍MAPCliff-WMGR,这是一个用于预测分子活动的计算框架,专门针对活动悬崖.
在涉及活动悬崖的场景中提高分子活动预测的准确性.

主要方法:

使用加权分子图和核心mGraphSNN_GAT模块,并进行特定模型的调整.
采用具有正弦转换的独立特征映射 (IFM) 模块来解决活动悬崖数据中的光谱偏差.
开发MACE-R7基准平台,用于评估预测性能.

主要成果:

对于悬崖分子,MAPCliff-WMGR实现了0.677的平均RMSE,表现比基线高7.2%.
该方法在MACE-R7基准上显示了总体3.2%的平均改善,而悬崖分子的平均改善为8.7%.
模型的解释性揭示了关键原子通过注意力分析和维度减少为活动悬崖做出贡献.

结论:

在活动悬崖的存在下,MAPCliff-WMGR有效地预测了分子活动.
该框架显示了虚拟药物查的潜力,如乳腺癌ERα抑制剂的案例研究所示.
这项研究强调了专门的计算方法对于应对诸如药物发现中的活动悬崖等挑战的重要性.

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