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Developing and discovering new drugs is a complex and resource-intensiveendeavor that often involves substantial costs, time investment, and safetyconcerns. A key aspect of drug discovery involves identifying novel drug-target(DT) interactions. Existing computational methods for predicting DTinteractions have primarily focused on binary classification tasks, aiming todetermine whether a DT pair interacts or not. However, protein-ligandinteractions exhibit a continuum of binding strengths, known as bindingaffinity, presenting a persistent challenge for accurate prediction. In thisstudy, 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) andthe incorporation of Contact Map information as an inductive bias withincurrent models. Through extensive experimentation, we demonstrate that ourproposed approaches outperform the baseline models considered in this study,presenting a compelling case for further development in this direction. Weanticipate that the insights gained from this work will significantly narrowthe search space for potential drugs targeting specific proteins, therebyaccelerating drug discovery. Code and data for PGraphDTA are available athttps://github.com/Yijia-Xiao/PgraphDTA/.

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