Drug-Target Indication Prediction by Integrating End-to-End Learning and Fingerprints

TL;DR

This paper introduces a multi-view deep learning model that integrates different molecular descriptors for improved drug-target interaction prediction, demonstrating promising results on benchmark datasets.

Contribution

It proposes an adversarially trained multi-view architecture combining differentiable and predefined descriptors for drug-target prediction, addressing representation challenges.

Findings

Enhanced prediction accuracy on benchmark datasets

Effective integration of multiple molecular descriptors

Potential for improved drug discovery pipelines

Abstract

Computer-Aided Drug Discovery research has proven to be a promising direction in drug discovery. In recent years, Deep Learning approaches have been applied to problems in the domain such as Drug-Target Interaction Prediction and have shown improvements over traditional screening methods. An existing challenge is how to represent compound-target pairs in deep learning models. While several representation methods exist, such descriptor schemes tend to complement one another in many instances, as reported in the literature. In this study, we propose a multi-view architecture trained adversarially to leverage this complementary behavior by integrating both differentiable and predefined molecular descriptors. We conduct experiments on clinically relevant benchmark datasets to demonstrate the potential of our approach.