Molecular docking can be used to predict how strongly small‐molecule binders and their
chemical derivatives bind to a macromolecular target using its available three‐dimensional …

Using an efficient iterative method, we have developed a distance‐dependent knowledge‐
based scoring function to predict protein–protein interactions. The function, referred to as …

Motivation: Accurately predicting the binding affinities of large sets of diverse protein–ligand
complexes is an extremely challenging task. The scoring functions that attempt such …

In this work, eight different scoring functions have been combined with the aim of improving
the prediction of protein− ligand binding conformations and affinities. The obtained scores …

Computational approaches to drug discovery can reduce the time and cost associated with
experimental assays and enable the screening of novel chemotypes. Structure-based drug …

We report here a robust automated active site detection, docking, and scoring (AADS)
protocol for proteins with known structures. The active site finder identifies all cavities in a …

Empirical scoring functions based on either molecular force fields or cheminformatics
descriptors are widely used, in conjunction with molecular docking, during the early stages …

Motivation Machine learning scoring functions for protein–ligand binding affinity prediction
have been found to consistently outperform classical scoring functions. Structure-based …

The accurate identification of Drug–Target Interactions (DTIs) remains a critical turning point
in drug discovery and understanding of the binding process. Despite recent advances in …

Inspired by the concept of knowledge-based scoring functions, a new quantitative structure−
activity relationship (QSAR) approach is introduced for scoring protein− ligand interactions …