Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
There is a growing body of evidence showing that machine learning regression results in
more accurate structure‐based prediction of protein‐ligand binding affinity. Docking …
more accurate structure‐based prediction of protein‐ligand binding affinity. Docking …
Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.
H Li, KS Leung, MH Wong, PJ Ballester - Molecular Informatics, 2015 - europepmc.org
There is a growing body of evidence showing that machine learning regression results in
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
[引用][C] Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets
H Li, KS Leung, MH Wong, PJ Ballester - Molecular Informatics, 2015 - cir.nii.ac.jp
Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity
Prediction by the Effective Exploitation of Larger Data Sets | CiNii Research CiNii 国立情報学 …
Prediction by the Effective Exploitation of Larger Data Sets | CiNii Research CiNii 国立情報学 …
Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets
H Li, KS Leung, MH Wong, PJ Ballester - Molecular Informatics, 2015 - hero.epa.gov
There is a growing body of evidence showing that machine learning regression results in
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
Improving autodock vina using random forest: The growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
H Li, LK Sak, MH Wong, PJ Ballester - Molecular Informatics, 2015 - ra.lib.hksyu.edu.hk
There is a growing body of evidence showing that machine learning regression results in
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
[PDF][PDF] Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
H Li, KS Leung, MH Wong, PJ Ballester - Molecular informatics, 2015 - drive.google.com
There is a growing body of evidence showing that machine learning regression results in
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.
H Li, KS Leung, MH Wong… - Molecular …, 2015 - search.ebscohost.com
There is a growing body of evidence showing that machine learning regression results in
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets
H Li, KS Leung, MH Wong… - Molecular …, 2015 - pubmed.ncbi.nlm.nih.gov
There is a growing body of evidence showing that machine learning regression results in
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …
more accurate structure-based prediction of protein-ligand binding affinity. Docking methods …