DeepBSP—a machine learning method for accurate prediction of protein–ligand docking structures
J Bao, X He, JZH Zhang - Journal of chemical information and …, 2021 - ACS Publications
… 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 …
deviation (rmsd) of a ligand docking … Unlike the binding affinity, the rmsd between the docking …
From machine learning to deep learning: Advances in scoring functions for protein–ligand 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 …
gradually emerged as a promising alternative for protein–ligand binding affinity prediction and …
Protein–ligand docking in the machine-learning era
… and development of classical and machine learning protein–ligand scoring functions. In …
machine learning scoring function ranging from descriptor-based models to deep learning …
machine learning scoring function ranging from descriptor-based models to deep learning …
Machine learning in computational docking
… 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 …
smart … The result of the docking process is a set of ligands ranked according to their predicted …
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
PJ Ballester, JBO Mitchell - Bioinformatics, 2010 - academic.oup.com
… 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 …
learning … This study was a valuable proof-of-concept that machine learning can produce useful …
Evaluation of docking machine learning and molecular dynamics methodologies for DNA-ligand systems
… , 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,…
learning … Finally, the selected ligands mono imidazole lexitropsin (42), netropsin (45), and N,…
Machine learning optimization of cross docking accuracy
EJ Bjerrum - Computational biology and chemistry, 2016 - Elsevier
… 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 …
machine learning approach and a manually curated database of ligands and cross docking …
Machine-learning methods for ligand–protein molecular docking
K Crampon, A Giorkallos, M Deldossi, S Baud… - Drug discovery today, 2022 - Elsevier
… 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 …
methods. We present concepts for ligand–protein docking that are also usable for other docking …
Molecular docking for drug discovery: Machine-learning approaches for native pose prediction of protein-ligand complexes
HM Ashtawy, NR Mahapatra - International Meeting on Computational …, 2013 - Springer
… 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. …
docking problem and we present docking-… , which dramatically improve docking performance. …
An overview of scoring functions used for protein–ligand interactions in molecular docking
J Li, A Fu, L Zhang - Interdisciplinary Sciences: Computational Life …, 2019 - Springer
… –ligand interactions with new classification scheme [13], which classifies the scoring functions
into physics-based, empirical, knowledge-based and machine learning-… machine-learning …
into physics-based, empirical, knowledge-based and machine learning-… machine-learning …
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