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10.1007/s10822-014-9748-9 http://scihub22266oqcxt.onion/10.1007/s10822-014-9748-9 C5600879!5600879 !24840854     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 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 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 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24840854 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Comput+Aided+Mol+Des 2014 ; 28 (6 ): 631-46 Nephropedia Template TP {{tp|p=24840854 |t=2014. Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers |pdf=|usr=}}{{24840854 }} gab.com Text Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers JO: J Comput Aided Mol Des http://www.kidney.de/pget.php?&tw=1&pmid=24840854 #FOAvip Compared to the current knowledge on cancer chemotherapeutic agents, only limited information is available on the ability of organic compounds, such as drugs and/or natural products, to prevent or delay the onset of cancer. In order to evaluate chemical chemopreventive potentials and design novel chemopreventive agents with low to no toxicity, we developed predictive computational models for chemopreventive agents in this study. First, we curated a database containing over 400 organic compounds with known chemoprevention activities. Based on this database, various random forest and support vector machine binary classifiers were developed. All of the resulting models were validated by cross validation procedures. Then, the validated models were applied to virtually screen a chemical library containing around 23,000 natural products and derivatives. We selected a list of 148 novel chemopreventive compounds based on the consensus prediction of all validated models. We further analyzed the predicted active compounds by their ease of organic synthesis. Finally, 18 compounds were synthesized and experimentally validated for their chemopreventive activity. The experimental validation results paralleled the cross validation results, demonstrating the utility of the developed models. The predictive models developed in this study can be applied to virtually screen other chemical libraries to identify novel lead compounds for the chemoprevention of cancers. Twit Text FOAVip Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers ????J Comput Aided Mol Des http://www.kidney.de/pget.php?&tw=1&pmid=24840854 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24840854 #FOAvip English Wikipedia <ref>{{Cite pmid|24840854 }}</ref> Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers #MMPMID24840854 Sprague B ; Shi Q ; Kim MT ; Zhang L ; Sedykh A ; Ichiishi E ; Tokuda H ; Lee KH ; Zhu H J Comput Aided Mol Des 2014[Jun]; 28 (6 ): 631-46 PMID24840854 show ga Compared to the current knowledge on cancer chemotherapeutic agents, only limited information is available on the ability of organic compounds, such as drugs and/or natural products, to prevent or delay the onset of cancer. In order to evaluate chemical chemopreventive potentials and design novel chemopreventive agents with low to no toxicity, we developed predictive computational models for chemopreventive agents in this study. First, we curated a database containing over 400 organic compounds with known chemoprevention activities. Based on this database, various random forest and support vector machine binary classifiers were developed. All of the resulting models were validated by cross validation procedures. Then, the validated models were applied to virtually screen a chemical library containing around 23,000 natural products and derivatives. We selected a list of 148 novel chemopreventive compounds based on the consensus prediction of all validated models. We further analyzed the predicted active compounds by their ease of organic synthesis. Finally, 18 compounds were synthesized and experimentally validated for their chemopreventive activity. The experimental validation results paralleled the cross validation results, demonstrating the utility of the developed models. The predictive models developed in this study can be applied to virtually screen other chemical libraries to identify novel lead compounds for the chemoprevention of cancers. |*Drug Design [MESH] |*Support Vector Machine [MESH] |Anticarcinogenic Agents/chemical synthesis/*chemistry/*pharmacology [MESH] |Biological Products/chemical synthesis/chemistry/pharmacology [MESH] |Computer-Aided Design [MESH] |Databases, Pharmaceutical [MESH] |Humans [MESH] |Models, Biological [MESH] |Neoplasms/drug therapy [MESH]Design, synthesis and experimental validation of novel potential chemo(epmc).pdf DeepDyve Pubget Overpricing