… machine-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 …

… With the rapid development of machine learning (ML) techniques, ML-based SFs have
gradually emerged as a promising alternative for protein–ligand binding affinity prediction and …

… and development of classical and machine learning protein–ligand scoring functions. In …
machine learning scoring function ranging from descriptor-based models to deep learning …

… the state-of-the-art machine learning (ML) techniques in computational docking. The use of
smart … The result of the docking process is a set of ligands ranked according to their predicted …

… As an alternative to modelling assumptions in scoring functions, non-parametric machine
learning … This study was a valuable proof-of-concept that machine learning can produce useful …

… , DOCK 6, and Consensus methodologies, respectively. In addition, we proposed a machine
learning … Finally, the selected ligands mono imidazole lexitropsin (42), netropsin (45), and N,…

… Benchmarks show that different docking programs can … docking power using a supervised
machine learning approach and a manually curated database of ligands and cross docking …

… protein–ligand) molecular docking because it covers an important selection of … docking
methods. We present concepts for ligand–protein docking that are also usable for other docking …

… ligand scoring and ranking problems [3, 4]. However, the focus of this work is on the ligand
docking problem and we present docking-… , which dramatically improve docking performance. …

… –ligand interactions with new classification scheme [13], which classifies the scoring functions
into physics-based, empirical, knowledge-based and machine learning-… machine-learning …