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作为基于图形的双层xLSTM框架,MolGraph-xLSTM用于增强分子表示和可解释性

Yan Sun^1,2, Yutong Lu³, Yan Yi Li³

¹Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada.

Communications chemistry

|September 29, 2025

概括

MolGraph-xLSTM通过使用双尺度图形方法有效地建模长距离相互作用,改善了药物发现的分子性质预测. 这种新的方法增强了特征提取,用于更好的计算药物设计.

科学领域:

计算化学是一种计算化学.
化学信息学化学信息学
机器学习在药物发现中的作用

背景情况:

预测分子性质对于加速药物发现至关重要.
图形神经网络 (GNN) 广泛用于分子表示学习,但与远程依赖性作斗争.
开发先进的计算方法对于提高药物发现效率至关重要.

研究的目的:

介绍MolGraph-xLSTM,一种基于图形的新型xLSTM模型,旨在改善分子性质预测.
为了增强特征提取,并有效地模拟分子中的远程相互作用.
为药物发现提供更有效的计算工具.

主要方法:

在原子层面和动机层面的尺度上处理分子图.
使用基于GNN的xLSTM框架,具有用于局部特征提取和多层信息聚合的跳跃知识.
改进嵌入使用多头混合专家 (MHMoE) 以提高表达力.
在 MoleculeNet 和 Therapeutics Data Commons (TDC) 基准中的 21 个数据集上验证模型.

主要成果:

在MoleculeNet上,MolGraph-xLSTM取得了显著的改进:分类的平均AUROC增加了3.18%,回归的RMSE减少了3.83%.
在TDC基准上,该模型显示AUROC平均改善2.56%和RMSE平均减少3.71%.
这些结果表明,与基线方法相比,在分类和回归任务中表现优越.

结论:

MolGraph-xLSTM有效地捕捉了远程分子相互作用,优于现有的GNN.
双尺度图形处理和MHMoE精细化有助于增强分子表示学习.
该模型在计算药物发现任务中显示出强大的可通用性和有效性.

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