… terms without considering specific protein–ligand interaction. The … Note that the scoring
function of Vina was trained on a set of … larger than 2 Å as negative pose. Kumar and coworkers …

… model learn how protein–ligand interactions affect the node … positive, and PDBbind negative
samples with the fixed ratio … In terms of the RE score, our method shows 9–10 times better …

… that a model trained on protein–ligand interactions (I model) … the PDBbind training set
on the scoring power of MPNN … parameter, notably for models trained only on protein–ligand …

… of this data set for benchmarking protein–ligand binding … parameters estimated from
experimental and simulated data … its own representation of the protein–ligand interaction in order …

… RASPD method and traditional scoring functions on a range … of non-covalent protein-ligand
interactions with a resolution … energy has the strongest negative correlation with the ligand …

… larger training sets, it should not be interpreted as a negative … more of a traditional
distance-based scoring function such as … Our method treats the protein–ligand interactions in a …

… Some ML scoring functions have been designed for a specific type of protein–ligand interaction,
such as G-protein … To generate a negative dataset, we need to create decoys that do not …

… In this study, we proposed a simple scoring function (called … model, we characterized the
protein-ligand interactions by the … affinity is represented by the negative logarithms (pK d ) of the …

… -E: Pairwise interaction data set, we further added negative … to 3DCNN protein-ligand scoring,
Vina, RF-Score, and … for the task of predicting protein-ligand interactions, the Graph-CNN …

… and scoring functions for protein–ligand interactions might … with a set of sphere points that
represent the negative image of … , which was originally trained for protein–ligand interactions. …