PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps
This work addresses the problem of accelerating drug discovery for researchers by providing more accurate predictions of drug-target interactions, though it appears incremental as it builds on existing methods with novel enhancements.
The paper tackled the challenge of predicting drug-target interactions by focusing on binding affinity as a continuum rather than binary classification, and it proposed enhancements using Protein Language Models and Contact Maps, demonstrating performance improvements over baseline models.
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/.