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#Chem c3000 22 chemicals update
Ideally, a system that could allow both searching of new chemical space and the ability to update the database to predict new routes would be the most powerful combination 36. By focussing on a new metric-based approach following chemical change, the system will be able to explore without bias thereby allowing machine learning based upon only sensor feedback rather than relying on prior knowledge. This approach could allow the elimination of bias which can prevent the human experimenter from doing a particular set of experiments.
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This is because the ability to search for reactivity, rather than following the constraints of a design-to-target approach might lead to the discovery of new reactions and molecules by following reactivity first. We therefore hypothesized that the ‘closed-loop' exploration of chemical space could be implemented using a simple approach using spectroscopic feedback focussing on the differences between the starting reagents and the products enabling the search for new molecules, reactions and synthetic pathways. This is because the process of organic synthesis is normally target-based, and reactivity searches are normally focussed on a particular transformation/optimization of a given reaction step 24, 25, 26, 27. To date, the development of automated systems in chemistry 35 has focussed on the generation of predesigned libraries, while the real-time experimental exploration of chemical reactivity remains underexplored. Despite these advances, the autonomous ‘closed-loop' searching of organic 33, 34 chemical space following chemical reactivity 33 has not yet been achieved, which means the discovery of new reactions and molecules is still largely in the domain of the chemist. Automation offers advantages such as reliable control over a chemical process increasing reproducibility 29 and has been used to enable the development of systems that allow the automatic optimization of the reaction conditions for known reactions 30, 31, 32. In addition, the availability of configurable robotics (for example, three-dimensional-printer-based chemical robots) 14, as well as a large variety of control systems 15, 16, 17, have been introduced for custom chemistry, target syntheses 18, 19, 20 and for optimization purposes 21, 22, 23, 24, 25, 26, 27, 28. Also, the development of flow systems for the synthesis of known molecules is well established and is particularly useful for automating chemical processes 10, as well as for developing safer procedures for working under more extreme conditions, or with highly reactive materials 11, 12, 13. To help speed up the process of chemical synthesis, numerous time-saving devices have been developed for today's modern chemical laboratory, including flow systems 4, 5, 6, automated chromatography columns, in-line analysis 7, 8 and combinatorial screening that uses spectrometry 9. The number of small organic molecules reported in the literature is >75 million 1, yet this only represents an infinitesimal fraction of the estimated 10 60 molecules available to search in chemical space 2, 3. We show the RSI correlates with reactivity and is able to search chemical space using the most reactive pathways.
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This allows the automatic navigation of a chemical network, leading to previously unreported molecules while needing only to do a fraction of the total possible reactions without any prior knowledge of the chemistry. The robotic system combines chemical handling, in-line spectroscopy and real-time feedback and analysis with an algorithm that is able to distinguish and select the most reactive pathways, generating a reaction selection index (RSI) without need for separate work-up or purification steps. Herein we present a system that can autonomously evaluate chemical reactivity within a network of 64 possible reaction combinations and aims for new reactivity, rather than a predefined set of targets.
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The exploration of chemical space for new reactivity, reactions and molecules is limited by the need for separate work-up-separation steps searching for molecules rather than reactivity.