Motivation Machine-learning scoring functions (SFs) have been found to outperform
standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of …

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

Background Accurately predicting the binding affinities of large sets of protein-ligand
complexes is a key challenge in computational biomolecular science, with applications in …

Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …

A major shortcoming of empirical scoring functions for protein–ligand complexes is the low
degree of correlation between predicted and experimental binding affinities, as frequently …

Scoring functions for the prediction of protein-ligand binding affinity have seen renewed
interest in recent years when novel machine learning and deep learning methods started to …

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, several deep learning …

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

Motivation The identification of compound–protein interactions (CPIs) is an essential step in
the process of drug discovery. The experimental determination of CPIs is known for a large …

Accurate determination of protein–ligand binding affinity is a fundamental problem in
biochemistry useful for many applications including drug design and protein–ligand docking …