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10.1186/s13321-017-0235-x http://scihub22266oqcxt.onion/10.1186/s13321-017-0235-x C5583141/?report=reader!5583141!29086083     free     free     free Deprecated: Implicit conversion from float 217.6 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=29086083|t=2017. Molecular de-novo design through deep reinforcement learning |pdf=|usr=}}{{29086083}} gab.com Text Molecular de-novo design through deep reinforcement learning JO: J Cheminform http://www.kidney.de/pget.php?&tw=1&pmid=29086083 #FOAvip This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.Graphical abstract.Electronic supplementary material: The online version of this article (doi:10.1186/s13321-017-0235-x) contains supplementary material, which is available to authorized users. Twit Text FOAVip Molecular de-novo design through deep reinforcement learning ????J Cheminform http://www.kidney.de/pget.php?&tw=1&pmid=29086083 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29086083 #FOAvip English Wikipedia <ref>{{Cite pmid|29086083}}</ref> Molecular de-novo design through deep reinforcement learning #MMPMID29086083 Olivecrona M; Blaschke T; Engkvist O; Chen H J Cheminform 2017[]; 9 (ä): ä PMID29086083show ga This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.Graphical abstract.Electronic supplementary material: The online version of this article (doi:10.1186/s13321-017-0235-x) contains supplementary material, which is available to authorized users. äMolecular de-novo design through deep reinforcement learning (epmc).pdf DeepDyve Pubget Overpricing