… 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, …

… training data rather than learning protein–ligand interactions. … Third, for negative sample
construction, we first constructed negative … function form and has fewer parameters, had the best …

… 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 …

… 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 …

… 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 …

… 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 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 …

… using them as training data for machine-learning scoring … task, a cutoff is set, which separates
good from bad poses, and the … size, which use a protein–ligand interaction as the center. …

… This way, the model cannot distinguish positive and negative … and can be modulated by
a parameter α to adjust the weight … the PDBscreen dataset, we trained a model using an …