Empirical scoring functions to calculate binding affinities of protein–ligand complexes have
been calibrated based on experimental structure and affinity data collected from public and …

Objective The objective of this paper is to highlight the state-of-the-art machine learning (ML)
techniques in computational docking. The use of smart computational methods in the life …

Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving
novel hit compounds in drug discovery. However, the accuracy of the current docking …

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 …

Conspectus In structure-based drug design, scoring functions are widely used for fast
evaluation of protein–ligand interactions. They are often applied in combination with …

The drug development is generally arduous, costly, and success rates are low. Thus, the
identification of drug-target interactions (DTIs) has become a crucial step in early stages of …

A major shortcoming of empirical scoring functions for protein–ligand complexes is the low
degree of correlation between predicted and experimental binding affinities, as frequently …

Here, the comparisons of performance of nine consensus scoring strategies, in which
multiple scoring functions were used simultaneously to evaluate candidate structures for a …

Scoring functions play an essential role in structure-based virtual screening. They are
required to guide the docking of candidate compounds to structures of receptor binding …

The past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …