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: Koes University of Pittsburgh 7bi2k 0.56 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 affinity 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: Koes University of Pittsburgh 7bi2k 0.56 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 affinity model, conformer generation performed with rdkit via https//github.com/dkoes/rdkit-scripts/rdconf.py , ensemble of receptors chosen via pocketome.

Techniques: Software

Journal: International Journal of Molecular Sciences

Article Title: Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies

doi: 10.3390/ijms24087360

Figure Lengend Snippet: Calculation of RMSD, AUC and enrichment values.

Article Snippet: Autodock SMINA 1.1.2 [ ] and Genetic Optimization for Ligand Docking (GOLD 5.3.0) were used for the docking screening.

Techniques:

Journal: International Journal of Molecular Sciences

Article Title: Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies

doi: 10.3390/ijms24087360

Figure Lengend Snippet: Venn diagram representation of ( A ) hit compounds yielded by QSAR-based screening, SMINA docking, GOLD docking, ROCS matching, and EON comparing; and ( B ) the compounds with desired properties kept by parallel screening, negative design, including physicochemical, PAINS, aggregates, and promiscuous filtering.

Article Snippet: Autodock SMINA 1.1.2 [ ] and Genetic Optimization for Ligand Docking (GOLD 5.3.0) were used for the docking screening.

Techniques:

Journal: Molecules

Article Title: In Search of Synergistic Insect Repellents: Modeling of Muscarinic GPCR Interactions with Classical and Bitopic Photoactive Ligands

doi: 10.3390/molecules27103280

Figure Lengend Snippet: ( a ) The homology model of Drosophila melanogaster mAChR-A receptor based on the UniProtKB P16395 (ACM1_DROME) sequence and human M1 structure template (PDB code: 5CXV). The orthosteric binding-site region is marked with a black dashed line. ( b – e ) SMINA molecular docking of insect repellents DEET ( b ) and IR3535 ( c ) and classical agonist muscarine ( d ) to the homology model shown in ( a ). Top views are presented. ( e ) Docking energy decomposition presented as SMINA scoring function (SSF) in kcal/mol shows interacting ligand residues of the mAChR-A orthosteric binding site.

Article Snippet: In the first step, we performed the molecular docking of eight ligands to the X-ray structure of human M1 mAChR (PDB code: 5CXV) using SMINA code [ ], a fork for AutoDock Vina [ ].

Techniques: Sequencing, Binding Assay

Journal: Molecules

Article Title: In Search of Synergistic Insect Repellents: Modeling of Muscarinic GPCR Interactions with Classical and Bitopic Photoactive Ligands

doi: 10.3390/molecules27103280

Figure Lengend Snippet: ( a ) The homology model of Drosophila melanogaster mAChR-A receptor based on the UniProtKB P16395 (ACM1_DROME) sequence and human M1 structure template (PDB code: 5CXV). The orthosteric binding-site region is marked with a black dashed line. ( b – e ) SMINA molecular docking of insect repellents DEET ( b ) and IR3535 ( c ) and classical agonist muscarine ( d ) to the homology model shown in ( a ). Top views are presented. ( e ) Docking energy decomposition presented as SMINA scoring function (SSF) in kcal/mol shows interacting ligand residues of the mAChR-A orthosteric binding site.

Article Snippet: 3D structures of the ligands were downloaded from PubChem [ ] and docked to the inactive structure of the whole M1 receptor protein (PDB code: 5CXV) using SMINA package [ ], a fork of Autodock Vina [ ] that provides enhanced support for minimization and scoring.

Techniques: Sequencing, Binding Assay

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