Multi-Representation Local-Global Deep Learning Architecture for Molecular Property Prediction

Molecular property prediction is a fundamental yet crucial task. It relies on molecular representation, which involves transforming molecular structures and features into a form that can be processed by computers. Common representation methods can be divided into two perspectives: global and local. However, using molecular representations from a single perspective leads to the problem of models focusing excessively on certain features while neglecting other important information, which limits the model's generalization ability and accuracy. To address this issue, this paper proposes a Multi-Representation Local-Global Molecular Property Prediction Model (MRLG). This model adopts a multi-branch architecture, deeply integrating SMILES, molecular fingerprints, molecular graphs, and molecular substructure information. First, a Global-Local Fusion (GLF) module is designed, which can integrate multiple representations and generate new, more comprehensive representations. Second, a detailed feature extraction module, Double-Cross Convolution Mould(DCC), is designed for the generated representations. Experiments conducted on various real-world datasets fully validate the effectiveness of the MRLG model. Moreover, results from branch and module ablation experiments further confirm the effectiveness of the proposed method. Overall, our model demonstrates a promising ability to accurately predict molecular properties, offering valuable insights for the design and optimization of novel compounds in various fields of material science and drug development.