… a certain biological target using a training set of compounds that … The BEDROC score
represents this by weighting the ROC … the learning of actual protein-ligand interactions to a greater …

… (the F parameter) from the query protein sequence and training … considerable diversity of
the training data (the highest number of … into positive and conditionally negative examples [40]. …

… on detailed protein–ligand interaction decomposition and … Empirical scoring functions are
typically trained on a data set of … the parameters for SVM training on the training set; (vi) using …

… To this end, we introduce ENS-Score as an ensemble … graph invariants that describe
protein–ligand interactions 27. PPS-… In other words, we employed 30 trained models from the three …

… This measures the negative average of the pairwise residue-… a significant role in protein-ligand
interactions as they control … F1 score is simply computed as the average of the F1 scores …

… ) or favorable protein–ligand interactions (which lead to negative … training set due to the
nature of fitting binding affinities to a small … The main challenges confronting docking and scoring …

… for representing protein–ligand interactions. For a given … parameter optimization on
CASF-2007 benchmark’s training … To this end, we collect a training set which includes both …

… training robust AI models to accurately predict protein-ligand … decomposed from the ACNN
scores and found that the … lower affinity by assigning negative scores on atoms with potentially …

… or computational scoring approaches… trained the machine for drugs against single protein
human serum albumin, we can conclude that ML models can predict protein–ligand interaction …

… and noises, with the negative effect from the noises becoming … describing the protein-ligand
interaction across the whole set of … Smina [54] and NNscore [55] are two reliable scoring …