Journal: Journal of Cheminformatics

Article Title: An automated framework for QSAR model building

doi: 10.1186/s13321-017-0256-5

Figure Lengend Snippet: Overview of automated QSAR modeling workflow

Article Snippet: The developed open source automated QSAR modeling KNIME workflow embeds all tools necessary to perform all steps of the QSAR life cycle by following best practicing methods [ , ].

Techniques:

Journal: Journal of Cheminformatics

Article Title: An automated framework for QSAR model building

doi: 10.1186/s13321-017-0256-5

Figure Lengend Snippet: Automated QSAR modeling methodology

Article Snippet: The developed open source automated QSAR modeling KNIME workflow embeds all tools necessary to perform all steps of the QSAR life cycle by following best practicing methods [ , ].

Techniques:

Journal: Journal of Cheminformatics

Article Title: An automated framework for QSAR model building

doi: 10.1186/s13321-017-0256-5

Figure Lengend Snippet: Input data set options. Overview of possible ways to submit input data to the automated QSAR modeling workflow

Article Snippet: The developed open source automated QSAR modeling KNIME workflow embeds all tools necessary to perform all steps of the QSAR life cycle by following best practicing methods [ , ].

Techniques:

Journal: Journal of Cheminformatics

Article Title: An automated framework for QSAR model building

doi: 10.1186/s13321-017-0256-5

Figure Lengend Snippet: Input parameters. Input configurations required before to run the workflow

Article Snippet: The developed open source automated QSAR modeling KNIME workflow embeds all tools necessary to perform all steps of the QSAR life cycle by following best practicing methods [ , ].

Techniques:

Journal: Journal of Cheminformatics

Article Title: An automated framework for QSAR model building

doi: 10.1186/s13321-017-0256-5

Figure Lengend Snippet: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MODI\_ssR^{2}$$\end{document} M O D I _ s s R 2 versus QSAR_PVE for 30 datasets. K is the number of nearest neighbors. a K = 3 and b K = 5. QSAR_PVE(IVS) is PVE score of externally validated models without feature selection (Full-model) and with selected features (SF-model). High correlation with SF-models QSAR_PVE suggests \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MODI\_ssR^{2}$$\end{document} M O D I _ s s R 2 is good modelability criteria. Weaker correlation between Full-model QSAR_PVE and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MODI\_ssR^{2}$$\end{document} M O D I _ s s R 2 emphasize the importance of feature selection to obtain actual and reliable predictive performance of QSAR model

Article Snippet: The developed open source automated QSAR modeling KNIME workflow embeds all tools necessary to perform all steps of the QSAR life cycle by following best practicing methods [ , ].

Techniques: Selection