The compound (S)-oxetan-2-ylmethyl tosylate 1 was identified as a key synthetic fragment for the introduction of the 2-substituted oxetane functionality in potential drug candidates under development in our laboratories. The focus of this paper is to highlight methodologies evaluated in our quest for synthetic routes to 2-substituted oxetanes suitable for enabling manufacture. Of the five routes investigated, three (Route 1A, Route 2, and Route 3) were successfully demonstrated in the laboratory. Subsequently, Route 3 was executed at scale to deliver metric ton quantities of the oxetane tosylate 1 as a solution in EtOAc, which was integrated into the synthesis of 2 and aforementioned drug candidates.

Low-cost self-driving labs (SDLs) offer faster prototyping, low-risk hands-on experience, and a test bed for sophisticated experimental planning software which helps us develop state-of-the-art SDLs.

The target compound PF-06878031 is a key structural fragment of a range of oral late-stage glucagon-like peptide-1 receptor agonists (GLP-1-RA) under development in our laboratories for the indications of type-2 diabetes mellitus (T2DM) and weight loss. This article describes the identification of a selective alkylation route and development of a process, capable of delivering multikilo quantities of PF-06878031. Process development afforded improved safety, increased yield, reduced step count, and lowered PMI. The new process has been scaled up at multiple facilities to generate >1.5MT of high purity PF-06878031.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTNegative Data in Data Sets for Machine Learning TrainingMichael P. MaloneyMichael P. MaloneyDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United StatesMore by Michael P. Maloneyhttps://orcid.org/0009-0001-3385-7567, Connor W. ColeyConnor W. ColeyDepartment of Chemical Engineering and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United StatesMore by Connor W. Coleyhttps://orcid.org/0000-0002-8271-8723, Samuel GenhedenSamuel GenhedenMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, SwedenMore by Samuel Genhedenhttps://orcid.org/0000-0002-7624-7363, Nessa CarsonNessa CarsonEarly Chemical Development, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield SK10 2NA, U.K.More by Nessa Carsonhttps://orcid.org/0000-0002-2769-1775, Paul HelquistPaul HelquistDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United StatesMore by Paul Helquisthttps://orcid.org/0000-0003-4380-9566, Per-Ola NorrbyPer-Ola NorrbyData Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, SwedenMore by Per-Ola Norrbyhttps://orcid.org/0000-0002-2419-0705, and Olaf Wiest*Olaf WiestDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States*Email: [email protected]More by Olaf Wiesthttps://orcid.org/0000-0001-9316-7720Cite this: Org. Lett. 2023, 25, 17, 2945–2947Publication Date (Web):April 26, 2023Publication History Received19 April 2023Published online26 April 2023Published inissue 5 May 2023https://pubs.acs.org/doi/10.1021/acs.orglett.3c01282https://doi.org/10.1021/acs.orglett.3c01282editorialACS PublicationsCopyright © Published 2023 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views7805Altmetric-Citations5LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (945 KB) Get e-AlertscloseSUBJECTS:Addition reactions,Chemical reactions,Machine learning,Materials,Organic synthesis Get e-Alerts

This review proposes the concept of a “frugal twin,” similar to a digital twin, but for physical experiments. Frugal twins range from simple toy examples to low-cost surrogates of high-cost research systems. For example, a color-mixing self-driving laboratory (SDL) can serve as a low-cost version of a costly multi-step chemical discovery SDL. Frugal twins already provide hands-on experience for SDLs with low costs and low risks. They can also offer as test beds for software prototyping (e.g., optimization, data infrastructure), and a low barrier to entry for democratizing SDLs. However, there is room for improvement. The true value of frugal twins can be realized in three core areas. Firstly, hardware and software modularity; secondly, purpose-built design (human-inspired vs. hardware-centric vs. human-in-the-loop); and thirdly state-of-the-art (SOTA) software (e.g., multi-fidelity optimization). We also describe the ethical benefits and risks that come with the democratization of science through frugal twins. For future work, we suggest ideas for new frugal twins, SDL educational course outcomes, and a classification scheme for autonomy levels.

A two-year collective effort towards the reduction by 50% of the usage of 7 hazardous solvents (Green Chemistry Principle #5) within a large-scale industrial R&D organization.

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.

Electrochemical transformations involve complex parameter interactions, ranging from universal chemistry variables such as solvent and reagents to specialist factors including electrode material and current density. Hence, the development of a robust and scale-independent electrochemical reaction can currently be a challenge. High-throughput experimentation (HTE) is an enabling method for reaction optimization and robustness testing. Here we provide an industrial and academic perspective on the state of the art of the combination of HTE with electrochemical reaction optimization for applications, including scale-up. We then present our vision for a future in which HTE reduces barriers to wide adoption of electrochemistry across the field of chemical synthesis.

Attempts to reproduce eight, putative, enantioselective dibromination and chlorohydroxylation reactions from oft-cited literature studies are described. The reactions were performed with full fidelity to the original report wherever possible. Analysis of the enantiomeric composition was performed by chiral stationary phase HPLC or SFC (CSP-HPLC or CSP-SFC), as opposed to the original report, which used chiral shift reagent NMR spectroscopy. After careful study, the reported levels of enantioselectivity were found to be incorrect. Possible explanations for the false positive results are discussed.