Molecular Docking

Molecular docking is a computational technique used to predict the binding affinity and conformation of small molecules (ligands) to target proteins, crucial for drug discovery. Current research emphasizes improving the accuracy and efficiency of docking through the development of novel deep learning models, including graph neural networks, transformers, and diffusion models, often incorporating physics-based scoring functions and active learning strategies to refine predictions. These advancements aim to accelerate virtual screening, enabling faster identification of potential drug candidates and enhancing the overall drug design process, particularly for challenging targets like those involved in protein-protein interactions.