MvMRL: a multi-view molecular representation learning method for molecular property prediction

作者：Ru Zhang, Yanmei Lin, Yijia Wu, Lei Deng, Hao Zhang, Mingzhi Liao, Yuzhong Peng · 发表于：Briefings in Bioinformatics · 年份：2024 · DOI：10.1093/bib/bbae298 · 被引用次数：98 · 研究领域：Computational Drug Discovery Methods、Machine Learning in Materials Science、Protein Structure and Dynamics

Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previous molecular representation learning studies often suffer from limitations, such as over-reliance on a single molecular representation, failure to fully capture both local and global information in molecular structure, and ineffective integration of multiscale features from different molecular representations. These limitations restrict the complete and accurate representation of molecular structure and properties, ultimately impacting the accuracy of predicting molecular properties. To this end, we propose a novel multi-view molecular representation learning method called MvMRL, which can incorporate feature information from multiple molecular representations and capture both local and global information from different views well, thus improving molecular property prediction. Specifically, MvMRL consists of four parts: a multiscale CNN-SE Simplified Molecular Input Line Entry System (SMILES) learning component and a multiscale Graph Neural Network encoder to extract local feature information and global feature information from the SMILES view and the molecular graph view, respectively; a Multi-Layer Perceptron network to capture complex non-linear relationship features from the molecular fingerprint view; and a dual cross-attentio...