… native protein–ligand complex is expressed as the negative … protein–ligand pairs in training,
validation and test sets. The … scoring framework for predicting protein–ligand interactions, …

… model for protein–ligand scoring that is trained to classify … distinct targets and 3251 negative
examples from 300 distinct … networks to score protein–ligand interactions using a direct, …

… docking scores and 4D based deep learning scoring method, we … rules of protein–ligand
interactions from the data by deep … -binder complexes as negative. Another possible solution is …

… that predicts proteinligand interaction by using both ligand … then use scoring functions to
estimate the binding affinities (energy … score of an active label being assigned to a true negative …

… negative samples are determined with RMSD. Protease data were largely directed into the
training set … We considered the top-scoring docked pose for each protein–ligand complex in …

… be estimated from a single configuration of the protein-ligand … data set, we now made use
of the original PDBbind training … to scoring functions for protein-ligand interactions and affinity …

… where \(U^{kl}(r)\) are unknown functions that are deduced from the training set of binding
affinities for protein-ligand complexes. From now on, we will call these functions scoring …

… non-bonded interactions, with the parameters estimated from the experiment data or QM [41]…
over 1.8 million data entries of experimental protein–ligand interaction data mostly from …

… the degree of protein-ligand interactions and is a useful … -based approach is the negative
natural logarithmic value of Kd … Training of atomic model for 35 epochs achieved MAE score of …

… since it was used as the “scoring power” benchmark in the … For training and performance
evaluation, the negative base-… The quality of docked poses was estimated by calculating the …