Abstract Structure-based virtual screening (SBVS) via docking has been used to discover
active molecules for a range of therapeutic targets. Chemical and protein data sets that …

Molecular docking predicts whether and how small molecules bind to a macromolecular
target using a suitable 3D structure. Scoring functions for structure‐based virtual screening …

Scoring functions are essential for modern in silico drug discovery. However, the accurate
prediction of binding affinity by scoring functions remains a challenging task. The …

Protein–ligand scoring functions are widely used in structure-based drug design for fast
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …

As more bioactivity and protein structure data become available, scoring functions (SFs)
using machine learning (ML) to leverage these data sets continue to gain further accuracy …

The predictive performance of classical scoring functions (SFs) seems to have reached a
plateau. Currently, SFs relying on sophisticated machine learning techniques have shown …

We discuss how data unbiasing and simple methods such as protein-ligand Interaction
FingerPrint (IFP) can overestimate virtual screening performance. We also show that IFP is …

Introduction The discovery of a new drug is a costly and lengthy endeavour. The
computational prediction of which small molecules can bind to a protein target can …

Molecular docking is a pragmatic approach to exploit protein structures for new ligand
discovery, but the growing size of available chemical space is increasingly challenging to …

Abstract Structure-based drug design depends on the detailed knowledge of the three-
dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of …