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Search for "in silico evolution" in Full Text gives 1 result(s) in Beilstein Journal of Organic Chemistry.

Biomimetic molecular design tools that learn, evolve, and adapt

David A Winkler

Beilstein J. Org. Chem. 2017, 13, 1288–1302, doi:10.3762/bjoc.13.125

methods and their potential impacts in chemistry, engineering, and medicine. Keywords: automated chemical synthesis; deep learning; evolutionary algorithms; in silico evolution; machine learning; materials design and development; neural networks; Introduction There is still not a clear understanding of

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Graphical Abstract

Figure 1: Hypothesized evolution of ‘life’ and ‘intelligence’.

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Figure 2: Structure of maitotoxin, one of the most complex natural products ever tackled by total synthesis. ...

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Figure 3: Hypothesis-driven closed-loop learning rationale for Adam and Eve. Hypothesis-driven experimentatio...

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Figure 4: Traditional backpropagation neural network machine learning algorithm.

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Figure 5: Comparison of architectures of shallow (non-deep) and deep neural networks. Adapted with permission...

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Figure 7: A high-throughput-materials synthesis and characterization facility RAMP, (Rapid Automated Material...

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Figure 8: An interactive procedure to design and 3D print bespoke reaction ware to optimization yield and pur...

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Figure 9: (Top) Photograph of a small-molecule synthesizer comprised of three modules for deprotection, coupl...

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Figure 10: An example of a composition-based descriptor vector that could be used to model or evolve materials...

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Figure 11: Example of a simple elitism (copy unchanged), crossover, and point mutation operations acting on th...

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Figure 12: A simple example of a two-dimensional fitness functions. The lines represent different evolutionary...

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Figure 13: Robotic synthesis and testing of populations of pathogen-resistant polymers evolved by a combinatio...

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Figure 14: Net deliverable energy as a function of porous material void fraction at 77 K cycling between 100 a...

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Review

Published 29 Jun 2017

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