Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction

Abstract

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.

References in corpus (12)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• DeepDTA: Deep Drug-Target Binding Affinity Prediction
• Molecular Graph Convolutions: Moving Beyond Fingerprints
• Convolutional Networks on Graphs for Learning Molecular Fingerprints
• DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
• AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
• Attentive Pooling Networks
• Obtaining Calibrated Probabilities from Boosting
• Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
• Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction
• Drug-Target Indication Prediction by Integrating End-to-End Learning and Fingerprints

Cited by in corpus (1)

• GNN-PT: Enhanced Prediction of Compound-protein Interactions by Integrating Protein Transformer