In recent years, the cheminformatics community has seen an increased success with
machine learning-based scoring functions for estimating binding affinities and pose …

Introduction: The ability to discriminate among ligands binding to the same protein target in
terms of their relative binding affinity lies at the heart of structure-based drug design. Any …

Accurately predicting protein–ligand binding affinities is an important problem in
computational chemistry since it can substantially accelerate drug discovery for virtual …

Computational drug discovery provides an efficient tool for helping large-scale lead
molecule screening. One of the major tasks of lead discovery is identifying molecules with …

The recently reported machine learning-or deep learning-based scoring functions (SFs)
have shown exciting performance in predicting protein–ligand binding affinities with fruitful …

The cornerstone of computational drug design is the calculation of binding affinity between
two biological counterparts especially a chemical compound, ie a ligand, and a protein …

In recent years, machine-learning-based scoring functions have significantly improved the
scoring power. However, many of these methods do not perform well in distinguishing the …

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …

Background Computer-aided drug design provides an effective method of identifying lead
compounds. However, success rates are significantly bottlenecked by the lack of accurate …

Protein–ligand interaction prediction presents a significant challenge in drug design.
Numerous machine learning and deep learning (DL) models have been developed to …