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 …

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

One key task in virtual screening is to accurately predict the binding affinity (delta-G) of
protein-ligand complexes. Recently, deep learning (DL) has significantly increased the …

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 …

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

We present a new approach to estimate the binding affinity from given three-dimensional
poses of protein–ligand complexes. In this scheme, every protein–ligand atom pair makes …

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 …

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient
and successful rational drug design. Therefore, many binding affinity prediction methods …

Evaluating the protein–ligand binding affinity is a substantial part of the computer-aided drug
discovery process. Most of the proposed computational methods predict protein–ligand …