… and rationally engineering protein–ligand interactions. … to rigorously estimate biophysical
parameters from massively … , we developed the quality score S training , which measures model …

… data of protein–ligand complexes, which allow the training of … for protein–ligand complexes
in each target-specific scoring … Fine-tuning the parameters in model-training stage using a …

… obtained the score s, and where A and B are parameters of the … We, therefore, conclude that
SE has a negative effect on the … Brier score loss as a function of training set size and scaling …

… In summary, problems with training data primarily relate to two … and can be modulated by
a parameter α to 289 adjust the … ligand binding poses and the negative sample 500 size with …

… The ability to accurately predict protein-ligand interactions continues … types of data that can
be used for modeling protein-ligand … bias scores based on the protein-ligand complexes and …

… the protein–ligand binding mode and associated affinity score for … It also provides
protein–ligand interaction patterns to … , positive charged, negative charged and metal coordination. …

… 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. …

… protein-ligand pair by the median IC50 value. SMILES strings were standardized and
canonicalized using the … This allows ML models to be trained on protein-ligand binding affinity …

… 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 …