We present a novel optimization approach to train a free-shape distance-dependent protein-
ligand scoring function called Convex-PL. We do not impose any functional form of the …

In recent years, machine learning has been proposed as a promising strategy to build
accurate scoring functions for computational docking finalized to numerically empowered …

Protein-protein docking protocols aim to predict the structures of protein-protein complexes
based on the structure of individual partners. Docking protocols usually include several …

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 …

Background Molecular docking is a widely-employed method in structure-based drug
design. An essential component of molecular docking programs is a scoring function (SF) …

Docking programs are widely used to discover novel ligands efficiently and can predict
protein− ligand complex structures with reasonable accuracy and speed. However, there is …

Predicting the native poses of ligands correctly is one of the most important steps towards
successful structure-based drug design. Binding affinities (BAs) estimated by traditional …

We assess the performance of several machine learning-based scoring methods at protein-
ligand pose prediction, virtual screening, and binding affinity prediction. The methods and …

The growing number of protein–ligand complex structures, particularly the structures of
proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major …

Motivation Despite the progress made in studying protein–ligand interactions and the
widespread application of docking and affinity prediction tools, improving their precision and …