Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Open accessAbstract
Motivation: In bioinformatics, machine learning-based methods that predict the compound–protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. words in natural language processing) using deep neural networks has demonstrated excellent performance on various difficult problems. For the CPI problem, data are provided as discrete symbolic data, i.e. compounds are represented as graphs where the vertices are atoms, the edges are chemical bonds, and proteins are sequences in which the characters are amino acids. In this study, we investigate the use of end-to-end representation learning for compounds and proteins, integrate the representations, and develop a new CPI prediction approach by combining a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins. Results: Our experiments using three CPI datasets demonstrated that the proposed end-to-end approach achieves competitive or higher performance as compared to various existing CPI prediction methods. In addition, the proposed approach significantly outperformed existing methods on an unbalanced dataset. This suggests that data-driven representations of compounds and proteins obtained by end-to-end GNNs and CNNs are more robust than traditional chemical and biological features obtained from databases. Although analyzing deep learning models is difficult due to their black-box nature, we address this issue using a neural attention mechanism, which allows us to consider which subsequences in a protein are more important for a drug compound when predicting its interaction. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model even when modeling is performed using real-valued representations instead of discrete features. Availability and implementation: https://github.com/masashitsubaki Supplementary information: Supplementary data are available at Bioinformatics online.
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Because ligand-receptor interactions are directed, they could be used to infer causal interactions of previously unknown ligand-receptor pairs.
“In fact, as ligand-receptor interactions are directed, they could be used to infer causal interactions of previously unknown ligand-receptor pairs [161, 163].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [163]
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- Authors
- Tsubaki, M., Tomii, K., & Sese, J.
- Affiliations
- Artificial Intelligence Research Center (JP)
- Tokyo Institute of Technology (JP)
- Corresponding author
- Tsubaki, Masashi
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-07-06
- Volume
- 35
- Issue
- 2
- Pages
- 309-318
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2860192827
- Cited by
- 688
- References
- 32
- Citations by year
- 2026: 72 · 2025: 116 · 2024: 125 · 2023: 110 · 2022: 115 · 2021: 67 · 2020: 63 · 2019: 17 · 2018: 2
- FWCI
- 27.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NEDO
- JSPS KAKENHI (JP17H07392, 15H01717)
- Platform Project for Supporting Drug Discovery and Life Science Research
- Basis for Supporting Innovative Drug Discovery and Life Science Research
- BINDS
- AMED (JP18am0101110)
- JST CREST (JPMJCR1689)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- compound-protein interaction prediction, graph neural networks, convolutional neural networks, virtual screening, drug discovery, attention mechanism, protein sequence representation, chemical graph representation, class imbalance, model interpretability
- MeSH terms
- Deep Learning, Proteins, Neural Networks, Computer, Protein Interaction Mapping, Drug Discovery
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