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10.3762/bjoc.13.125 http://scihub22266oqcxt.onion/10.3762/bjoc.13.125 C5496546!5496546!28694872     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Beilstein+J+Org+Chem 2017 ; 13 (ä): 1288-302 Nephropedia Template TP {{tp|p=28694872|t=2017. Biomimetic molecular design tools that learn, evolve, and adapt |pdf=|usr=}}{{28694872}} gab.com Text Biomimetic molecular design tools that learn, evolve, and adapt JO: Beilstein J Org Chem http://www.kidney.de/pget.php?&tw=1&pmid=28694872 #FOAvip A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known ?S curve?, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. Twit Text FOAVip Biomimetic molecular design tools that learn, evolve, and adapt ????Beilstein J Org Chem http://www.kidney.de/pget.php?&tw=1&pmid=28694872 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28694872 #FOAvip English Wikipedia <ref>{{Cite pmid|28694872}}</ref> Biomimetic molecular design tools that learn, evolve, and adapt #MMPMID28694872 Winkler DA Beilstein J Org Chem 2017[]; 13 (ä): 1288-302 PMID28694872show ga A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known ?S curve?, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. äBiomimetic molecular design tools that learn, evolve, and adapt (epmc).pdf DeepDyve Pubget Overpricing