Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 4, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa266
Keywords
deep learning; molecular representation; molecular property; compound-protein interaction; computational method; graph neural networks
Funding
- National Natural Science Foundation of China [21775060, 61872216, 61472205, 81630103, 31871071, 61836004]
- Turing AI Institute of Nanjing
- Beijing Brain Science Special Project [Z181100001518006]
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This study introduces TrimNet, a model that efficiently learns molecular representation through a triplet message mechanism, reducing the number of parameters and achieving superior results across multiple tasks.
Motivation: Computational methods accelerate drug discovery and play an important role in biomedicine, such as molecular property prediction and compound-protein interaction (CPI) identification. A key challenge is to learn useful molecular representation. In the early years, molecular properties are mainly calculated by quantum mechanics or predicted by traditional machine learning methods, which requires expert knowledge and is often labor-intensive. Nowadays, graph neural networks have received significant attention because of the powerful ability to learn representation from graph data. Nevertheless, current graph-based methods have some limitations that need to be addressed, such as large-scale parameters and insufficient bond information extraction. Results: In this study, we proposed a graph-based approach and employed a novel triplet message mechanism to learn molecular representation efficiently, named triplet message networks (TrimNet). We show that TrimNet can accurately complete multiple molecular representation learning tasks with significant parameter reduction, including the quantum properties, bioactivity, physiology and CPI prediction. In the experiments, TrimNet outperforms the previous state-of-the-art method by a significant margin on various datasets. Besides the few parameters and high prediction accuracy, TrimNet could focus on the atoms essential to the target properties, providing a clear interpretation of the prediction tasks. These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning.
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