… 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 …

… parameters that fit the observed binding affinities of protein−ligand complexes of a given
training set. … from known structural data to directly estimate the total protein−ligand binding free …

… 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 …

… of protein–ligand interactions by setting the parameter as “… Selection of training data set is
always crucial for the OLS … be due to the fact that the bad data points has been removed. More …

… Five free energy terms (four adjustable parameters because … protein-ligand interactions can
be translated by the negative … ], was trained from a larger number of pairs of protein-ligand …

… 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 …

… training algorithm iterates parameter estimation and ligand pose optimization. The initial
random parameter … penalties for steric overlap by explicitly modeling negative data. However, …

… 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 …