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10.1002/minf.201700153 http://scihub22266oqcxt.onion/10.1002/minf.201700153 C5838524!5838524!29319225     free     free     free 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       Mol+Inform 2018 ; 37 (1-2): ä Nephropedia Template TP {{tp|p=29319225|t=2018. De Novo Design of Bioactive Small Molecules by Artificial Intelligence |pdf=|usr=}}{{29319225}} gab.com Text De Novo Design of Bioactive Small Molecules by Artificial Intelligence JO: Mol Inform http://www.kidney.de/pget.php?&tw=1&pmid=29319225 #FOAvip Generative artificial intelligence offers a fresh view on molecular design. We present the first?time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine?tuned on recognizing retinoid X and peroxisome proliferator?activated receptor agonists. We synthesized five top?ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low?micromolar receptor modulatory activity in cell?based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. Twit Text FOAVip De Novo Design of Bioactive Small Molecules by Artificial Intelligence ????Mol Inform http://www.kidney.de/pget.php?&tw=1&pmid=29319225 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29319225 #FOAvip English Wikipedia <ref>{{Cite pmid|29319225}}</ref> De Novo Design of Bioactive Small Molecules by Artificial Intelligence #MMPMID29319225 Merk D; Friedrich L; Grisoni F; Schneider G Mol Inform 2018[Jan]; 37 (1-2): ä PMID29319225show ga Generative artificial intelligence offers a fresh view on molecular design. We present the first?time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine?tuned on recognizing retinoid X and peroxisome proliferator?activated receptor agonists. We synthesized five top?ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low?micromolar receptor modulatory activity in cell?based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. äDe Novo Design of Bioactive Small Molecules by Artificial Intelligence(epmc).pdf DeepDyve Pubget Overpricing