Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry

• URL: http://arxiv.org/abs/2401.03369v2
• Date: Tue, 9 Jan 2024 02:20:04 GMT
• Title: Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry
• Authors: Zeyu Wang, Tianyi Jiang, Jinhuan Wang, Qi Xuan
• Abstract summary: Deep learning-based molecular property prediction has emerged as a solution to the resource-intensive nature of traditional methods. In this paper, we propose a novel multi-modal representation learning model, called SGGRL, for molecular property prediction. To ensure consistency across modalities, SGGRL is trained to maximize the similarity of representations for the same molecule while minimizing similarity for different molecules.
• Score: 6.049566024728809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular property prediction refers to the task of labeling molecules with some biochemical properties, playing a pivotal role in the drug discovery and design process. Recently, with the advancement of machine learning, deep learning-based molecular property prediction has emerged as a solution to the resource-intensive nature of traditional methods, garnering significant attention. Among them, molecular representation learning is the key factor for molecular property prediction performance. And there are lots of sequence-based, graph-based, and geometry-based methods that have been proposed. However, the majority of existing studies focus solely on one modality for learning molecular representations, failing to comprehensively capture molecular characteristics and information. In this paper, a novel multi-modal representation learning model, which integrates the sequence, graph, and geometry characteristics, is proposed for molecular property prediction, called SGGRL. Specifically, we design a fusion layer to fusion the representation of different modalities. Furthermore, to ensure consistency across modalities, SGGRL is trained to maximize the similarity of representations for the same molecule while minimizing similarity for different molecules. To verify the effectiveness of SGGRL, seven molecular datasets, and several baselines are used for evaluation and comparison. The experimental results demonstrate that SGGRL consistently outperforms the baselines in most cases. This further underscores the capability of SGGRL to comprehensively capture molecular information. Overall, the proposed SGGRL model showcases its potential to revolutionize molecular property prediction by leveraging multi-modal representation learning to extract diverse and comprehensive molecular insights. Our code is released at https://github.com/Vencent-Won/SGGRL.

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