… 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 …

… , false positives, true negatives, and false negatives in a binary … score or pK d predictions for
each molecule in the test set. We … that correspond to protein–ligand interactions. One avenue …

… Nevertheless, methods used to score and rank virtual hits … of the partition function Z; a negative
value of G indicates that energy is … training dataset. It is also important to note that the vast …

… By training on a generic protein-ligand dataset, we seek to … with a finite number of parameters,
and which are trained by (mini-… to further extract attention scores, which are intermediate …

… : the training set for learning the trainable parameters, the validation … protein–ligand complexes
for validating docking/scoring … Negative examples were obtained by random sampling of …

… “scores”, through this neural network we can estimate the “… the data set TS50 which is excluded
from all the training data … too many parameters, which can cause overfitting and negative …

… Compared with protein–ligand complexes, (28) we have much … To derive the scoring function,
we use a total of 230 RNA–… (6 Å) along the positive and negative x, y, and z directions (a …

… in protein folding and protein-ligand systems. This novel … As the fraction of data used as the
training set was increased … polar-polar residue interaction with negative coefficients. A greater …

… to estimate binding affinities of protein-ligand interactions. SMILES (… value of the lowest was
added to all negative scores. … The model was trained with the learned parameters to deliver a …

… and dynamics information of protein-ligand interactions at the … top scoring docked output for
each protein-ligand complex. … benchmark MD dataset for machine-learning training that can …