Drug Target Pair

Drug target pair research focuses on predicting the binding affinity between drugs and their target proteins, a crucial step in drug discovery. Current efforts leverage deep learning models, frequently employing graph neural networks (GNNs) and transformers to integrate structural and sequential information from both drug and target molecules, often incorporating techniques like contrastive learning and dynamic prompting to enhance prediction accuracy and interpretability. These advancements aim to accelerate drug development by reducing reliance on time-consuming experimental methods, enabling more efficient screening of potential drug candidates and ultimately leading to more effective therapies. Improved model interpretability is also a key focus, allowing researchers to understand the mechanisms underlying drug-target interactions.