PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps

TL;DR

This paper introduces PGraphDTA, a novel method that combines Protein Language Models and Contact Maps to improve the prediction of drug-target binding affinities, aiming to accelerate drug discovery processes.

Contribution

The study proposes integrating protein language models and contact map information into DTI prediction models, demonstrating improved performance over baseline methods.

Findings

Outperforms baseline models in predicting binding affinities

Effectively incorporates contact maps as inductive bias

Accelerates drug discovery by narrowing search space

Abstract

Developing and discovering new drugs is a complex and resource-intensive endeavor that often involves substantial costs, time investment, and safety concerns. A key aspect of drug discovery involves identifying novel drug-target (DT) interactions. Existing computational methods for predicting DT interactions have primarily focused on binary classification tasks, aiming to determine whether a DT pair interacts or not. However, protein-ligand interactions exhibit a continuum of binding strengths, known as binding affinity, presenting a persistent challenge for accurate prediction. In this study, we investigate various techniques employed in Drug Target Interaction (DTI) prediction and propose novel enhancements to enhance their performance. Our approaches include the integration of Protein Language Models (PLMs) and the incorporation of Contact Map information as an inductive bias within current models. Through extensive experimentation, we demonstrate that our proposed approaches outperform the baseline models considered in this study, presenting a compelling case for further development in this direction. We anticipate that the insights gained from this work will significantly narrow the search space for potential drugs targeting specific proteins, thereby accelerating drug discovery. Code and data for PGraphDTA are available at https://github.com/Yijia-Xiao/PgraphDTA/.