Computational approaches to drug discovery can reduce the time and cost associated with
experimental assays and enable the screening of novel chemotypes. Structure-based drug …

One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …

Protein-ligand scoring is an important step in a structure-based drug design pipeline.
Selecting a correct binding pose and predicting the binding affinity of a protein-ligand …

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

Scoring functions are important components in molecular docking for structure-based drug
discovery. Traditional scoring functions, generally empirical-or force field-based, are robust …

Training machine learning algorithms with protein–ligand descriptors has recently gained
considerable attention to predict binding constants from atomic coordinates. Starting from a …

Accurate determination of target-ligand interactions is crucial in the drug discovery process.
In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting …

Predicting accurate protein–ligand binding affinities is an important task in drug discovery
but remains a challenge even with computationally expensive biophysics-based energy …

We assess the ability of our convolutional neural network (CNN)-based scoring functions to
perform several common tasks in the domain of drug discovery. These include correctly …

Computer-aided protein–ligand binding predictions are a valuable help in drug discovery.
Protein–ligand docking programs generally consist of two main components: a scoring …