… dataset bias, some studies report that ML-based scoring … network trained using frequentist
maximum likelihood estimation … abundant information on protein-ligand interactions, which can …

… questionable at low identity scores because of the degrading … were trained on six data splits
to predict protein-ligand … split also had a bad performance with R 2 of 0.42 and RMSE of 1.0 …

… .com with additional details about the score and the social media … an optimized set of ligand
partial atomic changes. Three … were then built using tleap and force field parameters, and …

… In this category we find classical scoring functions that use … description of the protein–ligand
interaction landscape did not … A negative consequence of this is the fact that the latter do …

… ML-based scoring functions have been developed using known protein-ligand inhibitor data
… experimentally validated negative data set, which have not been used in training our ML …

… a general model for protein–ligand binding affinity prediction. … We then scored the provided
pose of the molecule and each … (22) or maximum likelihood estimation, could provide better …

… obtained crystal structure of protein–ligand complex, the … classified as positive and
negative samples by the model. … the training set, the validation set, and the internal test set in …

… while randomly selecting a decoy pose from the same protein–ligand pair as the negative. …
of protein–ligand interactions and ensuring the effectiveness of deep learning scoring …

… and regenerate parameters for a specific FF using the drop-… in discriminating bad
protein–ligand interactions from the good … to docking scoring. In addition, we confirmed these …

… extract features of the protein–ligand interaction. This is a … generated with the predicted
protein–ligand binding affinity score, pK a … The binding affinity, pK a , is expressed as the negative …