LAUSR

In recent years, thanks to advances in computer hardware and dataset availability, data‐driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructing a new scoring function, a function that can predict the binding score for a generated protein‐ligand pose during docking procedure or a crystal complex, based on machine and deep learning has become an active research area in computer‐aided drug design. GB‐Score is a state‐of‐the‐art machine learning‐based scoring function that utilizes distance‐weighted interatomic contact features, PDBbind‐v2019 general set, and Gradient Boosting Trees algorithm to the binding affinity prediction. The distance‐weighted interatomic contact featurization method used the distance between different ligand and protein atom types for numerical representation of the protein‐ligand complex. GB‐Score attains Pearson's correlation 0.862 and RMSE 1.190 on the CASF‐2016 benchmark test in the scoring power metric. GB‐Score's codes are freely available on the web at https://github.com/miladrayka/GB_Score.