Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
| Autor: | Masashi Tsubaki, Jun Sese, Kentaro Tomii |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Computer science Machine learning computer.software_genre Biochemistry Convolutional neural network 03 medical and health sciences Deep Learning End-to-end principle Drug Discovery Protein Interaction Mapping Molecular Biology 030304 developmental biology 0303 health sciences Virtual screening Artificial neural network Drug discovery business.industry Deep learning 030302 biochemistry & molecular biology Proteins Computer Science Applications Visualization Computational Mathematics ComputingMethodologies_PATTERNRECOGNITION Computational Theory and Mathematics Neural Networks Computer Artificial intelligence business Feature learning computer |
| Zdroj: | Bioinformatics. 35:309-318 |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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