Biomimetic molecular design tools that learn, evolve, and adapt
journal contributionposted on 13.07.2021, 04:25 by David Winkler
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.
JournalBeilstein Journal of Organic Chemistry
Pagination15p. (p. 1288-1302)
Rights StatementThe Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.
Science & TechnologyPhysical SciencesChemistry, OrganicChemistryautomated chemical synthesisdeep learningevolutionary algorithmsin silico evolutionmachine learningmaterials design and developmentneural networksCHEMICAL MARKUP LANGUAGESELF-ORGANIZATIONDESCRIPTOR SELECTIONBACTERIAL ATTACHMENTNEURAL-NETWORKDISCOVERYQSAROPTIMIZATIONEVOLUTIONREACTIONWAREOrganic Chemistry