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10.1126/sciadv.aap7885 http://scihub22266oqcxt.onion/10.1126/sciadv.aap7885 C6059760!6059760 !30050984     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=30050984 &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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\30050984 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Adv 2018 ; 4 (7 ): eaap7885 Nephropedia Template TP {{tp|p=30050984 |t=2018. Deep reinforcement learning for de novo drug design |pdf=|usr=}}{{30050984 }} gab.com Text Deep reinforcement learning for de novo drug design JO: Sci Adv http://www.kidney.de/pget.php?&tw=1&pmid=30050984 #FOAvip We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks-generative and predictive-that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only. Generative models are trained with a stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo-generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the RL approach to bias the generation of new chemical structures toward those with the desired physical and/or biological properties. In the proof-of-concept study, we have used the ReLeaSE method to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity, or toward compounds with inhibitory activity against Janus protein kinase 2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties. Twit Text FOAVip Deep reinforcement learning for de novo drug design ????Sci Adv http://www.kidney.de/pget.php?&tw=1&pmid=30050984 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30050984 #FOAvip English Wikipedia <ref>{{Cite pmid|30050984 }}</ref> Deep reinforcement learning for de novo drug design #MMPMID30050984 Popova M ; Isayev O ; Tropsha A Sci Adv 2018[Jul]; 4 (7 ): eaap7885 PMID30050984 show ga We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks-generative and predictive-that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only. Generative models are trained with a stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo-generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the RL approach to bias the generation of new chemical structures toward those with the desired physical and/or biological properties. In the proof-of-concept study, we have used the ReLeaSE method to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity, or toward compounds with inhibitory activity against Janus protein kinase 2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties. |*Deep Learning [MESH] |*Drug Design [MESH] |Janus Kinase 2/antagonists & inhibitors/metabolism [MESH] |Models, Molecular [MESH] |Neural Networks, Computer [MESH] |Quantitative Structure-Activity Relationship [MESH]Deep reinforcement learning for de novo drug design (epmc).pdf DeepDyve Pubget Overpricing