The development of a new knowledge-based scoring function (DrugScore) and its power to
recognize binding modes close to experiment, to predict binding affinities, and to identify 'hot …

Machine learning scoring functions for protein–ligand binding affinity have been found to
consistently outperform classical scoring functions when trained and tested on crystal …

Docking tools to predict whether and how a small molecule binds to a target can be applied
if a structural model of such target is available. The reliability of docking depends, however …

We introduce the new knowledge-based scoring function DSX that consists of distance-
dependent pair potentials, novel torsion angle potentials, and newly defined solvent …

The protein–ligand scoring function plays an important role in computer-aided drug
discovery and is heavily used in virtual screening and lead optimization. In this study, we …

Bulky, flexible molecules such as peptides and peptidomimetics are often used as lead
compounds during the drug discovery process. Pathophysiological events, eg, the formation …

Following the pioneering work of Geoffrey Hilton about the application of a deep neural
network to analyze handwritten digits [1], we have seen a boom in the use of machine …

Selection of putative binding poses is a challenging part of virtual screening for protein–
protein interactions. Predictive models to filter out binding candidates with the highest …

In computational structure-based drug design, the scoring functions are the cornerstones to
the success of design/discovery. Many approaches have been explored to improve their …

The virtual screening of large numbers of compounds against target protein binding sites
has become an integral component of drug discovery workflows. This screening is often …