… learning (ML) techniques, ML-based SFs have gradually emerged as a promising alternative
for protein–ligand … Emergence of more data-hungry deep learning (DL) approaches in …

… In this work, we have developed a new set of deep learning models that take the output of
docking programs as input. Our models can be fashioned to classify activity (virtual screening) …

… both select the best possible docking protocol given a protein–ligand pair and to provide
insight into which protein–ligand pairs will result in a better pose given a docking protocol. We …

… the ligand-binding process. Here we present a deep learning model for flexible protein-ligand
docking … Our method introduces a new approach for the reconstruction of ligand poses in …

… essential feature of the ligand-binding process. Flexible protein–ligand docking still remains
a … , we present a deep learning (DL) model for flexible protein–ligand docking based on the …

… -learning model, named DeepBSP, that can directly predict the root mean square deviation
(rmsd) of a ligand docking … Unlike the binding affinity, the rmsd between the docking poses …

… introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking …
DD appears to clearly bias final sets toward high docking scores, and since active ligands …

… Deep learning protocols for ligand docking. (A) Predicting the protein–ligand (PL) distance
… His research focuses on the applications of deep learning in ligand docking and virtual …

… Deep learning has been known to learn representations and patterns in … deep learning
to predict binding affinity of protein-nonpeptide ligand interaction without the need of a docked …

… DeepVS learns abstract features that are suitable to discriminate between active ligands …
deep learning to improve docking-based virtual screening. Recent works on improving docking-…