Multi-view self-attention for interpretable drug–target interaction prediction
| Autor: | Ebenezer Nanor, Brighter Agyemang, Lei Chen, Michael Y. Kpiebaareh, Zhihua Lei, Wei-Ping Wu |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Machine Learning (stat.ML) Health Informatics Machine learning computer.software_genre Machine Learning (cs.LG) Machine Learning 03 medical and health sciences 0302 clinical medicine Drug Development Statistics - Machine Learning Drug Discovery Feature (machine learning) Drug Interactions 030212 general & internal medicine Representation (mathematics) 030304 developmental biology Interpretability 0303 health sciences business.industry Drug discovery Pipeline (software) Computer Science Applications Pharmaceutical Preparations Drug development Benchmark (computing) Artificial intelligence business computer Feature learning |
| Zdroj: | Journal of Biomedical Informatics. 110:103547 |
| ISSN: | 1532-0464 |
| DOI: | 10.1016/j.jbi.2020.103547 |
| Popis: | The drug discovery stage is a vital aspect of the drug development process and forms part of the initial stages of the development pipeline. In recent times, machine learning-based methods are actively being used to model drug-target interactions for rational drug discovery due to the successful application of these methods in other domains. In machine learning approaches, the numerical representation of molecules is critical to the performance of the model. While significant progress has been made in molecular representation engineering, this has resulted in several descriptors for both targets and compounds. Also, the interpretability of model predictions is a vital feature that could have several pharmacological applications. In this study, we propose a self-attention-based multi-view representation learning approach for modeling drug-target interactions. We evaluated our approach using three benchmark kinase datasets and compared the proposed method to some baseline models. Our experimental results demonstrate the ability of our method to achieve competitive prediction performance and offer biologically plausible drug-target interaction interpretations. |
| Databáze: | OpenAIRE |
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