… 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, …

… 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 …

… 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 …

… for new data, a sufficient amount of training data is required. The … model with existing scoring
functions on the same test set. All … sets, whereas the remainder were labeled as negative …

… of coefficients and parameters that are estimated from … splitting node, is the only parameter
used to fine tune our model. … to develop ET-Score by training it on docking data to evaluate its …

… on the Glide scoring function value, if “bad” contacts were not … -Score-based descriptors for
each protein–ligand complex in … By integrating intermediate-state protein–ligand interaction …