… the choice of parameters did not depend on the training set, we … Errors were estimated with
the standard deviation (SD), … is the use of a single-point approach to estimate a value that is a …

… Training machine learning algorithms with protein–ligand … not learning any type of
protein–ligand interaction, since we do … of either positive or negative weights assigned to favorable …

… but negative partial charge, which is repulsive to the negative … over-estimate due to protein
homologs between training and … DEKOIS 2.0 dataset to evaluate DLIGAND2 and RF-Score-…

… detailed energetic investigation of protein–ligand interaction. … the scoring function, which is
widely used to estimate quickly … for 10 protein–ligand complexes in the training set to 10 ns. …

… derive our scoring function for protein-ligand interactions, … training set, we used randomly
chosen 80% of protein-ligand … with amide oxygen; c negatively charged oxygen with positively …

… R p and/or R s values (minimum is negative one). Another measure of … scoring performances
of NN and RF SFs on the training set … protein-ligand interactions. Thus we find that BsN-…

… negative ligands for targets, as exhibited in the last section. In addition, different datasets may
have their own protein-ligand interaction space, and models trained on … (a docking scoring …

… been applied to encode protein–ligand interactions. The fingerprint … The DNN model was
trained with tunable parameters that … the same set of training, validation, and test data that had …

… of use of K DEEP makes it already an attractive scoring function … In our case, the total number
of learnable parameters adds … this by using the PDBbind full minus core set as training and …

… docking due to deficiency of scoring functions (SFs) and … that characterize protein–ligand
interactions and experimental … difficult set because most poses are marked as the negatives…