Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone.
We describe a system, ChemSCAD, for the creation of digital reactors based on the chemical operations, physical parameters, and synthetic sequence to produce a given target compound, to show that the system can translate the gram-scale batch synthesis of the antiviral compound Ribavirin (yield 43% over three steps), the narcolepsy drug Modafinil (yield 60% over three steps), and both batch and flow instances of the synthesis of the anticancer agent Lomustine (batch yield 65% over two steps) in purities greater than or equal to 96%. The syntheses of compounds developed using the ChemSCAD system, including reactor designs and analytical data, can be stored in a database repository, with the information necessary to critically evaluate and improve upon reactionware syntheses being easily shared and versioned.
Modern science has developed well-defined and versatile sets of chemicals to perform many specific tasks, yet the diversity of these reagents is so large that it can be impractical for any one lab to stock everything they might need. At the same time, isssues of stability or limited supply mean these chemicals can be very expensive to purchase from specialist retailers. Here, we address this problem by developing a cartridge -oriented approach to reactionware-based chemical generators which can easily and reliably produce specific reagents from low-cost precursors, requiring minimal expertise and time to operate, potentially in low infrastructure environments. We developed these chemical generators for four specific targets; transition metal catalyst precursor tris(dibenzylideneacetone)dipalladium(0) [Pd2(dba)3], oxidising agent Dess-Martin periodinane (DMP), protein photolinking reagent succinimidyl 4,4’-azipentanoate (NHS-diazirine), and the polyoxometalate cluster {P8W48}. The cartridge synthesis of these materials provides high-quality target compounds in good yields which are suitable for subsequent utilization.
Robotic systems for synthetic chemistry are becoming common but they are expensive, fixed to a narrow set of reactions, and must be used within a complex laboratory environment. A portable system that could synthesize known molecules anywhere, on demand, in a fully automated way could revolutionize access to important molecules. Herein, we present a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control. We demonstrate the system by synthesizing five small organic molecules, four oligopeptides, and four oligonucleotides, in good yields and purities with a total of 24,936 base steps executed over 329 h of platform runtime.
<p>Digital chemistry aims to define a hard link from the top abstraction layer in chemistry down to the synthesis, but this is difficult in traditional glassware since it is not possible to explicitly link the architecture with the unit operations. By 3D printing the synthesis modules in the precise order to affect the synthesis, it is possible to create digitally encoded reactors for chemical synthesis in ‘reactionware’. However, creation of these devices requires a specific skillset for CAD modelling which few synthetic chemists have. Herein, we describe an intuitive system, ChemSCAD, for the creation of digital reactor models based on the chemical operations, physical parameters and synthetic sequence to produce a given target compound. We demonstrate the ability of the ChemSCAD system to translate the gram-scale batch synthesis of the anti-viral compound Ribavirin (yield 43% over three steps), the narcolepsy drug Modafinil (yield 60% over three steps), and both batch and flow instances of the synthesis of the anti-cancer agent Lomustine (batch yield 65% over two steps) in purities ≥96%. The syntheses of compounds developed using the ChemSCAD system, including reactor designs and analytical data, can be stored in a single database repository where all the information necessary to critically evaluate, and improve upon, reactionware syntheses can be easily shared and versioned.</p>
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