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10.1186/s13321-017-0215-1 http://scihub22266oqcxt.onion/10.1186/s13321-017-0215-1 C5418185/?report=reader!5418185!29086046     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Cheminform 2017 ; 9 (ä): ä Nephropedia Template TP {{tp|p=29086046|t=2017. ChemSAR: an online pipelining platform for molecular SAR modeling |pdf=|usr=}}{{29086046}} gab.com Text ChemSAR: an online pipelining platform for molecular SAR modeling JO: J Cheminform http://www.kidney.de/pget.php?&tw=1&pmid=29086046 #FOAvip Background: In recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers. Results: This work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files. Conclusion: ChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.Graphical abstract. Electronic supplementary material: The online version of this article (doi:10.1186/s13321-017-0215-1) contains supplementary material, which is available to authorized users. Twit Text FOAVip ChemSAR: an online pipelining platform for molecular SAR modeling ????J Cheminform http://www.kidney.de/pget.php?&tw=1&pmid=29086046 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29086046 #FOAvip English Wikipedia <ref>{{Cite pmid|29086046}}</ref> ChemSAR: an online pipelining platform for molecular SAR modeling #MMPMID29086046 Dong J; Yao ZJ; Zhu MF; Wang NN; Lu B; Chen AF; Lu AP; Miao H; Zeng WB; Cao DS J Cheminform 2017[]; 9 (ä): ä PMID29086046show ga Background: In recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers. Results: This work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files. Conclusion: ChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.Graphical abstract. Electronic supplementary material: The online version of this article (doi:10.1186/s13321-017-0215-1) contains supplementary material, which is available to authorized users. äChemSAR: an online pipelining platform for molecular SAR modeling (epmc).pdf DeepDyve Pubget Overpricing