Digitizing Chemical Synthesis in 3D Printed Reactionware.

Chemistry digitization requires an unambiguous link between experiments and the code used to generate the experimental conditions and outcomes, yet this process is not standardized, limiting the portability of any chemical code. What is needed is a universal approach to aid this process using a well-defined standard that is composed of syntheses that are employed in modular hardware. Herein we present a new approach to the digitization of organic synthesis that combines process chemistry principles with 3D printed reactionware. This approach outlines the process for transforming unit operations into digitized hardware and well-defined instructions that ensure effective synthesis. To demonstrate this, we outline the process for digitizing 3 MIDA boronate building blocks, an ester hydrolysis, a Wittig olefination, a Suzuki-Miyaura coupling reaction, and synthesis of the drug sulfanilamide.

Automatic Generation of 3D-Printed Reactionware for Chemical Synthesis Digitization using ChemSCAD.

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.

3D designed and printed chemical generators for on demand reagent synthesis.

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.

Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments.

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.

A Multicomponent Route to Functionalized Amides and Oxazolidinones.

An organobase-mediated multicomponent reaction of unactivated esters, epoxides, and amines is reported, furnishing functionalized amide derivatives. A wide range of substrates are tolerated under the reaction conditions, including chiral epoxides, which react with no erosion of enantiopurity. Facile modification of the method through replacing the ester derivative with dimethyl carbonate enables access to the corresponding oxazolidinone derivatives.