Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction
This work addresses interpretable prediction of drug-target interactions for drug discovery, representing an incremental improvement in domain-specific applications.
The authors tackled drug-target interaction prediction by proposing a multi-view self-attention method, achieving competitive performance on three kinase benchmark datasets and providing biologically plausible interpretations.
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.