… the prediction of the essential parameters, such as the binding … as well as the scoring functions
evaluated on scoring tasks. … nature of training samples, (2) lack of negative samples, and (…

… scoring functions [13, 16]. However, IFPs might fail to detect key … to predict the complete
training set (ie 90% of the total data set). … for other hyper-parameters. Feature importance was …

… in MLSFs and the characterization of protein–ligand interactions are always limited, which …
training process, SVM was used to find the best hyperplane to divide the positive and negative …

… , in the comparative assessment of scoring functions (CASF) 2016, … physics of the
protein–ligand interaction as desired. For … can set a threshold for ΔG of a protein–ligand pair as …

… chemists to investigate protein–ligand interactions at the atomic level … We manually checked
any data points with a negative … the models trained on BindingNet could improve the scoring …

… of scoring functions is crucial for distinguishing good and bad … the pairwise protein–ligand
interactions and protein–ligand … loss function was set to L2 loss, the penalty parameter C of …

… is its scoring function (SF), which is required to estimate the … of 9299 protein–ligand complexes
while the validation set … of the protein–ligand complexes denoted as pK, the negative base…

… Color gradient represents experimental potency (negative log-… score of 0.92 on the
protein-ligand functional effect test set. … -training stage, we categorized protein-ligand interactions …

… , lipophilic protein–ligand interactions and an improved estimation … Thus, we also trained
general scoring functions using all the … shown to be negative on MDM2 and Bcl2 interactions via …

… structure information of protein–ligand interactions, but they … docking score was retained for
each protein–ligand pair, thus … the negative log-likelihood values of all protein–ligand atom …