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10.1186/s13321-017-0206-2 http://scihub22266oqcxt.onion/10.1186/s13321-017-0206-2 C5359269/?report=reader!5359269!29086122     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29086122&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 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=29086122|t=2017. Nonpher: computational method for design of hard-to-synthesize structures |pdf=|usr=}}{{29086122}} gab.com Text Nonpher: computational method for design of hard-to-synthesize structures JO: J Cheminform http://www.kidney.de/pget.php?&tw=1&pmid=29086122 #FOAvip In cheminformatics, machine learning methods are typically used to classify chemical compounds into distinctive classes such as active/nonactive or toxic/nontoxic. To train a classifier, a training data set must consist of examples from both positive and negative classes. While a biological activity or toxicity can be experimentally measured, another important molecular property, a synthetic feasibility, is a more abstract feature that can?t be easily assessed. In the present paper, we introduce Nonpher, a computational method for the construction of a hard-to-synthesize virtual library. Nonpher is based on a molecular morphing algorithm in which new structures are iteratively generated by simple structural changes, such as the addition or removal of an atom or a bond. In Nonpher, molecular morphing was optimized so that it yields structures not overly complex, but just right hard-to-synthesize. Nonpher results were compared with SAscore and dense region (DR), other two methods for the generation of hard-to-synthesize compounds. Random forest classifier trained on Nonpher data achieves better results than models obtained using SAscore and DR data.Electronic supplementary material: The online version of this article (doi:10.1186/s13321-017-0206-2) contains supplementary material, which is available to authorized users. Twit Text FOAVip Nonpher: computational method for design of hard-to-synthesize structures ????J Cheminform http://www.kidney.de/pget.php?&tw=1&pmid=29086122 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29086122 #FOAvip English Wikipedia <ref>{{Cite pmid|29086122}}</ref> Nonpher: computational method for design of hard-to-synthesize structures #MMPMID29086122 Vor?ilák M; Svozil D J Cheminform 2017[]; 9 (ä): ä PMID29086122show ga In cheminformatics, machine learning methods are typically used to classify chemical compounds into distinctive classes such as active/nonactive or toxic/nontoxic. To train a classifier, a training data set must consist of examples from both positive and negative classes. While a biological activity or toxicity can be experimentally measured, another important molecular property, a synthetic feasibility, is a more abstract feature that can?t be easily assessed. In the present paper, we introduce Nonpher, a computational method for the construction of a hard-to-synthesize virtual library. Nonpher is based on a molecular morphing algorithm in which new structures are iteratively generated by simple structural changes, such as the addition or removal of an atom or a bond. In Nonpher, molecular morphing was optimized so that it yields structures not overly complex, but just right hard-to-synthesize. Nonpher results were compared with SAscore and dense region (DR), other two methods for the generation of hard-to-synthesize compounds. Random forest classifier trained on Nonpher data achieves better results than models obtained using SAscore and DR data.Electronic supplementary material: The online version of this article (doi:10.1186/s13321-017-0206-2) contains supplementary material, which is available to authorized users. äNonpher: computational method for design of hard-to-synthesize structu(epmc).pdf DeepDyve Pubget Overpricing