Drug Target Affinity Prediction

Drug target affinity prediction aims to computationally estimate how strongly a drug molecule binds to its target protein, a crucial step in drug discovery. Current research focuses on improving prediction accuracy using various deep learning approaches, including graph neural networks to leverage 3D protein structures and techniques like k-nearest neighbors and multi-task learning to enhance model performance and address data scarcity. Accurate prediction can significantly accelerate drug development by reducing the need for expensive and time-consuming experimental screening, ultimately leading to faster and more cost-effective drug discovery.