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10.1021/acscentsci.6b00367 http://scihub22266oqcxt.onion/10.1021/acscentsci.6b00367 C5408335!5408335 !28470045     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28470045 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       ACS+Cent+Sci 2017 ; 3 (4 ): 283-293 Nephropedia Template TP {{tp|p=28470045 |t=2017. Low Data Drug Discovery with One-Shot Learning |pdf=|usr=}}{{28470045 }} gab.com Text Low Data Drug Discovery with One-Shot Learning JO: ACS Cent Sci http://www.kidney.de/pget.php?&tw=1&pmid=28470045 #FOAvip Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. MODEL: 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016). Twit Text FOAVip Low Data Drug Discovery with One-Shot Learning ????ACS Cent Sci http://www.kidney.de/pget.php?&tw=1&pmid=28470045 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28470045 #FOAvip English Wikipedia <ref>{{Cite pmid|28470045 }}</ref> Low Data Drug Discovery with One-Shot Learning #MMPMID28470045 Altae-Tran H ; Ramsundar B ; Pappu AS ; Pande V ACS Cent Sci 2017[Apr]; 3 (4 ): 283-293 PMID28470045 show ga Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. MODEL: 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016). äLow Data Drug Discovery with One-Shot Learning (epmc).pdf DeepDyve Pubget Overpricing