Journal: Scientific Reports

Article Title: Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

doi: 10.1038/s41598-024-58122-7

Figure Lengend Snippet: Test R \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document} 2 -scores in the prediction of Smina docking scores for MAO-A and MAO-B inhibitors.

Article Snippet: The Smina docking software version 2020.12.10 ( https://sourceforge.net/projects/smina/ ) was used to perform molecular docking.

Techniques:

Journal: Scientific Reports

Article Title: Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

doi: 10.1038/s41598-024-58122-7

Figure Lengend Snippet: Enrichment curves calculated for Smina docking results and three best ML models on the testing set.

Article Snippet: The Smina docking software version 2020.12.10 ( https://sourceforge.net/projects/smina/ ) was used to perform molecular docking.

Techniques:

Journal: Scientific Reports

Article Title: Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

doi: 10.1038/s41598-024-58122-7

Figure Lengend Snippet: Comparison of the VS time using different methods.

Article Snippet: The Smina docking software version 2020.12.10 ( https://sourceforge.net/projects/smina/ ) was used to perform molecular docking.

Techniques: Comparison

Journal: Journal of computer-aided molecular design

Article Title: D3R Grand Challenge 3: Blind Prediction of Protein-Ligand Poses and Affinity Rankings

doi: 10.1007/s10822-018-0180-4

Figure Lengend Snippet: Top 3 submissions, based on Kendall’s τ, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.

Article Snippet: Zou University of Missouri-Columbia 87mci 0.23 docking performed with smina static binary available at https//sourceforge.net/projects/smina/files/ with default scoring function, then rescoring performed using gnina commit b3fa6ae13fc6b42924f49b2d751d68f1bc14bc08 available from https//github.com/gnina/gnina and the default cnn scoring model, conformer generation performed with rdkit via https//github.com/dkoes/rdkit-scripts/rdconf.py , ensemble of receptors chosen via pocketome.

Techniques: Software

Journal: Journal of computer-aided molecular design

Article Title: D3R Grand Challenge 3: Blind Prediction of Protein-Ligand Poses and Affinity Rankings

doi: 10.1007/s10822-018-0180-4

Figure Lengend Snippet: Top 3 submissions, based on Matthews correlation coefficient, for each affinity ranking challenge. Submission ID in bold font indicates a method that used machine learning. See for details.

Article Snippet: Zou University of Missouri-Columbia 87mci 0.23 docking performed with smina static binary available at https//sourceforge.net/projects/smina/files/ with default scoring function, then rescoring performed using gnina commit b3fa6ae13fc6b42924f49b2d751d68f1bc14bc08 available from https//github.com/gnina/gnina and the default cnn scoring model, conformer generation performed with rdkit via https//github.com/dkoes/rdkit-scripts/rdconf.py , ensemble of receptors chosen via pocketome.

Techniques: Software

Journal: eLife

Article Title: Probing the ionotropic activity of glutamate GluD2 receptor in HEK cells with genetically-engineered photopharmacology

doi: 10.7554/eLife.59026

Figure Lengend Snippet:

Article Snippet: Software, algorithm , Smina , https://sourceforge.net/projects/smina/ , , .

Techniques: Transfection, Construct, Software

Journal: Journal of chemical information and modeling

Article Title: Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening

doi: 10.1021/acs.jcim.7b00153

Figure Lengend Snippet: Search and Scoring Strategies used by Docking Programs

Article Snippet: The Smina fork of AutoDock Vina 37 was used.

Techniques: