Abstract Machine learning-based scoring functions (MLSFs) have shown potential for
improving virtual screening capabilities over classical scoring functions (SFs). Due to the …

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually
emerged as a promising alternative to overcome the weaknesses of classical SFs. However …

In this study, we developed a new physical-based scoring function, Atom Pair-Based
Scoring function (APBScore), which includes pairwise van der Waals (VDW), electrostatic …

Abstract Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged
as a promising alternative for protein–ligand binding affinity prediction and structure-based …

In structure-based virtual screening (SBVS), it is critical that scoring functions capture protein–
ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a …

The evaluation of ligand conformations is a crucial aspect of structure-based virtual
screening, and scoring functions play significant roles in it. While consensus scoring (CS) …

Abstract Structure-based virtual screenings (SBVSs) play an important role in drug discovery
projects. However, it is still a challenge to accurately predict the binding affinity of an …

Scoring functions play an important role in structure-based virtual screening. It has been
widely accepted that target-specific scoring functions (TSSFs) may achieve better …

A major shortcoming of empirical scoring functions is that they often fail to predict binding
affinity properly. Removing false positives of docking results is one of the most challenging …

Machine-learning-based scoring functions (MLSFs) have gained attention for their potential
to improve accuracy in binding affinity prediction and structure-based virtual screening …