  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/.
