Foundation Models in Action: Transforming Protein-Ligand Interaction Predictions for Drug Design

This study establishes a novel framework to advance the prediction of protein-ligand binding sites by leveraging state-of-the-art foundation models, including ESM-2 and ProtT5. By integrating multi-modal features such as sequence-based, structure-based, and physicochemical properties, this framework aims to address critical challenges in molecular biology and drug discovery. The developed system will be applied to accurately identify protein-ligand binding sites, which are pivotal in understanding molecular interactions and facilitating drug design. This is the first study to systematically adapt foundation models for protein-ligand binding predictions, combining the power of advanced AI with explainable methodologies to ensure interpretability and usability. Through this research, we aim to contribute to academic development by providing a generalizable framework that can be extended to other bioinformatics tasks, such as protein-protein interaction predictions or ligand classification. The societal and economic impact of this study is profound, as it accelerates the drug discovery process by identifying critical binding regions more accurately and efficiently. This advancement can significantly reduce the time and cost of developing new therapeutics, benefitting pharmaceutical industries and improving public health. Furthermore, the development of an open-access platform ensures that this innovative tool is widely available to researchers, fostering collaboration and enabling further breakthroughs in computational biology.