Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction Prediction

TL;DR

This paper introduces BarlowDTI, a novel deep learning approach using the Barlow Twins architecture combined with gradient boosting for accurate, efficient 1D drug-target interaction prediction, with interpretability and benchmarking against existing methods.

Contribution

The paper presents a new method that leverages Barlow Twins for feature extraction and gradient boosting for prediction, achieving state-of-the-art results with minimal input data.

Findings

Achieves state-of-the-art performance on multiple benchmarks.

Effectively exploits protein structural features from 1D data.

Provides an accessible web interface for drug-target interaction prediction.

Abstract

Accurate prediction of drug-target interactions is critical for advancing drug discovery. By reducing time and cost, machine learning and deep learning can accelerate this laborious discovery process. In a novel approach, BarlowDTI, we utilise the powerful Barlow Twins architecture for feature-extraction while considering the structure of the target protein. Our method achieves state-of-the-art predictive performance against multiple established benchmarks using only one-dimensional input. The use of gradient boosting machine as the underlying predictor ensures fast and efficient predictions without the need for substantial computational resources. We also investigate how the model reaches its decision based on individual training samples. By comparing co-crystal structures, we find that BarlowDTI effectively exploits catalytically active and stabilising residues, highlighting the model's ability to generalise from one-dimensional input data. In addition, we further benchmark new baselines against existing methods. Together, these innovations improve the efficiency and effectiveness of drug-target interaction predictions, providing robust tools for accelerating drug development and deepening the understanding of molecular interactions. Therefore, we provide an easy-to-use web interface that can be freely accessed at https://www.bio.nat.tum.de/oc2/barlowdti .