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

arXiv: https://arxiv.org/abs/2005.00397 · HTML: https://mlsanity.com/paper/2005.00397/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 3 of 3)

- [Improving evidential deep learning via multi-task learning](https://mlsanity.com/paper/2112.09368/) (2021)
- [Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring](https://mlsanity.com/paper/2202.01195/) (2022)
- [Deep Inverse Reinforcement Learning for Structural Evolution of Small Molecules](https://mlsanity.com/paper/2008.11804/) (2020)

## Abstract (arXiv)

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.