Protein Ligand Binding Affinity Prediction

Predicting protein-ligand binding affinity is crucial for drug discovery, aiming to accurately estimate how strongly a drug candidate will bind to its target protein. Current research heavily utilizes machine learning, employing diverse approaches such as graph convolutional networks, transformer-based models, and various neural network architectures, often incorporating both 2D ligand features and 3D protein-ligand structural information. These methods are being refined to improve accuracy, reduce reliance on large labeled datasets, and handle the complexities of diverse bioassay data and noisy labels. Advances in this field directly impact drug development by enabling faster, more efficient identification and optimization of potential drug candidates.