Abstract
The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture—the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation—that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.
Highlights
The exploration and optimization of chemical reactions can be a laborious and time-consuming endeavor[1] as poor optimization strategies, combined with human intuition laced with biases, means that a large number of reactions must be undertaken to map a given chemical space.[2]
Attention has turned to addressing these problems through initiatives which aim to normalize data generation and improve data sharing standards as well as applying novel methods for analyzing available reaction data, including machine learning.[5−11] These efforts can be seen as first steps toward the full digitization of chemistry (Figure 1).[12]
The synthesis of organic small molecules is still largely performed by hand in a laboratory setting that has barely changed in decades, but experts see the digitization of synthesis fast approaching.[13,60]
Summary
The exploration and optimization of chemical reactions can be a laborious and time-consuming endeavor[1] as poor optimization strategies, combined with human intuition laced with biases, means that a large number of reactions must be undertaken to map a given chemical space.[2]. On a deeper level the digitization of chemistry involves the full control of chemical processes by capturing all relevant input parameters, process operations and output data, and representing these in a machine-readable fashion to allow consistent reproduction of processes and efficient dissemination of the knowledge obtained.[13−16] We envision that future digital chemistry laboratories will run automated, multistep reactions on a variety of interoperable hardware. Analytics, it will be essential to capture all the key parameters of a preparation or experiment, including the context of the work This approach will be essential for reproducibility, with only a few exceptions, this data has not been recorded reliably to date. It will be vital to connect this via a standard data structure to a wide range of affordable, modular synthetic, and analytical hardware (Figure 2).[4,14] This architecture must work in tandem with feedback algorithms to provide the key features required for process optimization and the acquisition of large reaction data sets.[24]
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