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MolGramTreeNet:一种通过语法树受约束分子表示的多式分子性质预测模型

Yekang Zhang¹, Weichen Liu², Huijuan Zhao³

¹School of Information Science and Technology, Nantong University, Nantong, Jiangsu 226019, China.

|March 5, 2026

概括

MolGramTreeNet通过使用语法树来表示化学结构来增强药物发现的分子性质预测. 这种人工智能方法比现有方法提高了准确性和可解释性.

科学领域:

计算化学是一种计算化学.
人工智能在药物发现中的作用

背景情况:

分子性质预测对于有效的药物发现至关重要.
目前使用线性SMILES或2D图形的方法往往无法捕获明确的层次结构和化学语法.
这种限制阻碍了准确的预测模型的开发.

研究的目的:

推出MolGramTreeNet,这是一款旨在改善分子性质预测的新型多式联络框架.
为了利用语法树导向表示,更全面地了解分子结构.
提高人工智能驱动药物发现的准确性和可解释性.

主要方法:

开发了MolGramTreeNet,这是一个框架,可以使用无上下文语法将SMILES字符串解析为等级树.
采用双路径编码器:用于基于树的语义的变压器和用于2D拓的图形卷积网络.
融合了层次和拓特征,以产生强大的分子表示.

主要成果:

在十个不同的数据集中,MolGramTreeNet表现出卓越的性能.
与八个基线模型相比,实现了2.86%至21.47%的显著性能改善.
废除研究验证了语法树组件的关键贡献.

结论:

拟议的MolGramTreeNet框架通过结合化学语法和层次结构,有效地解决了现有方法的局限性.
该模型提供了更高的准确性和可解释性,标志着人工智能驱动药物发现的重大进步.
这种方法有望加速新药候选药物的识别.

关键词:

计算化学是一种计算化学.材料化学材料化学分子分子分子分子

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