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多模式交叉注意力分子属性预测用于文本,序列,图形和几何学

Shihao Sun¹, Peng Wang¹, Yunjiangcan He¹

¹School of Computer Science and Technology, Changchun University of Science and Technology, No. 7186, Weixing Road, Chaoyang District, Changchun 130022, China.

|December 1, 2025

概括

这项研究引入了一种用于预测分子性能的多式模式模型,通过整合多种分子数据表示来提高药物发现和材料设计的准确性. 多模态交叉注意力分子性质预测 (MCMPP) 模型增强了对单模态方法的预测能力.

科学领域:

计算化学计算化学
化学信息学化学信息学
药物发现药物发现药物发现
材料科学材料科学材料科学

背景情况:

标准定量结构-属性关系 (QSPR) 模型通常依赖于单模分子表示,限制了它们的预测准确性.
准确的QSPR模型对于加速药物发现和材料设计的步伐至关重要.
整合不同的分子数据类型可以潜在地克服单模态方法的局限性.

研究的目的:

开发和评估一种用于分子性质预测的新型多式联络模型.
通过整合多个分子表示来提高QSPR模型的准确性.
为了证明该模型在药物发现和材料设计应用中的有效性.

主要方法:

介绍了多模式交叉注意力分子性质预测 (MCMPP) 模型.
集成了各种分子表示:SMILES,ECFP指纹,分子图形和3D形状.
在由变压器编码器,BiLSTM,GCN和减少的Unimol+独立处理后使用交叉注意力机制.

主要成果:

在四个数据集 (Delaney,Lipophilicity,SAMPL,BACE) 中,MCMPP显示出更好的预测准确性.
该模型有效地利用了来自不同分子模式的互补信息.
MCMPP的性能优于其他数据融合方法,实现了最高的皮尔森相关系数.

结论:

多模式集成显著提高了分子性质预测的准确性.
MCMPP模型为加速药物发现和材料设计提供了一个强大的工具.
交叉注意力机制是有效的融合不同的分子数据表示.

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