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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