1
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Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, Lo S, Pablo-García S, Rajaonson EM, Skreta M, Yoshikawa N, Corapi S, Akkoc GD, Strieth-Kalthoff F, Seifrid M, Aspuru-Guzik A. Self-Driving Laboratories for Chemistry and Materials Science. Chem Rev 2024; 124:9633-9732. [PMID: 39137296 PMCID: PMC11363023 DOI: 10.1021/acs.chemrev.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
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Affiliation(s)
- Gary Tom
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Stefan P. Schmid
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, Vladimir-Prelog-Weg 1, CH-8093 Zurich, Switzerland
| | - Sterling G. Baird
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Yang Cao
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Kourosh Darvish
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Han Hao
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Stanley Lo
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Sergio Pablo-García
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
| | - Ella M. Rajaonson
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Marta Skreta
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Naruki Yoshikawa
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Samantha Corapi
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Gun Deniz Akkoc
- Forschungszentrum
Jülich GmbH, Helmholtz Institute
for Renewable Energy Erlangen-Nürnberg, Cauerstr. 1, 91058 Erlangen, Germany
- Department
of Chemical and Biological Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Egerlandstr. 3, 91058 Erlangen, Germany
| | - Felix Strieth-Kalthoff
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- School of
Mathematics and Natural Sciences, University
of Wuppertal, Gaußstraße
20, 42119 Wuppertal, Germany
| | - Martin Seifrid
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Department
of Materials Science and Engineering, North
Carolina State University, Raleigh, North Carolina 27695, United States of America
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
- Department
of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Department
of Materials Science & Engineering, University of Toronto, Toronto, Ontario M5S 3E4, Canada
- Lebovic
Fellow, Canadian Institute for Advanced
Research (CIFAR), 661
University Ave, Toronto, Ontario M5G 1M1, Canada
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2
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Sagmeister P, Melnizky L, Williams JD, Kappe CO. Simultaneous reaction- and analytical model building using dynamic flow experiments to accelerate process development. Chem Sci 2024; 15:12523-12533. [PMID: 39118626 PMCID: PMC11304546 DOI: 10.1039/d4sc01703j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/29/2024] [Indexed: 08/10/2024] Open
Abstract
In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies encompass prolonged timelines for the development of both a reaction model and analytical models. The development of both methods are often separated into different departments and can require an iterative optimization process. Addressing this issue, we introduce an innovative dual modeling approach, combining the development of a Process Analytical Technology (PAT) strategy with reaction optimization. This integrated approach is exemplified in diverse amidation reactions and the synthesis of the API benznidazole. The platform, characterized by a high degree of automation and minimal operator involvement, achieves PAT calibration through a "standard addition" approach. Dynamic experiments are executed to screen a broad process space and gather data for fitting kinetic parameters. Employing an open-source software program facilitates rapid kinetic parameter fitting and additional in silico optimization within minutes. This highly automated workflow not only expedites the understanding and optimization of chemical processes, but also holds significant promise for time and resource savings within the pharmaceutical industry.
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Affiliation(s)
- Peter Sagmeister
- Institute of Chemistry, University of Graz, NAWI Graz Heinrichstrasse 28 8010 Graz Austria
- Center for Continuous Flow Synthesis and Processing (CC FLOW), Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 8010 Graz Austria
| | - Lukas Melnizky
- Institute of Chemistry, University of Graz, NAWI Graz Heinrichstrasse 28 8010 Graz Austria
- Center for Continuous Flow Synthesis and Processing (CC FLOW), Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 8010 Graz Austria
| | - Jason D Williams
- Institute of Chemistry, University of Graz, NAWI Graz Heinrichstrasse 28 8010 Graz Austria
- Center for Continuous Flow Synthesis and Processing (CC FLOW), Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 8010 Graz Austria
| | - C Oliver Kappe
- Institute of Chemistry, University of Graz, NAWI Graz Heinrichstrasse 28 8010 Graz Austria
- Center for Continuous Flow Synthesis and Processing (CC FLOW), Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 8010 Graz Austria
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3
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Taylor CJ, Felton KC, Wigh D, Jeraal MI, Grainger R, Chessari G, Johnson CN, Lapkin AA. Accelerated Chemical Reaction Optimization Using Multi-Task Learning. ACS CENTRAL SCIENCE 2023; 9:957-968. [PMID: 37252348 PMCID: PMC10214532 DOI: 10.1021/acscentsci.3c00050] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Indexed: 05/31/2023]
Abstract
Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.
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Affiliation(s)
- Connor J. Taylor
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom
- Innovation
Centre in Digital Molecular Technologies, Yusuf Hamied Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United
Kingdom
| | - Kobi C. Felton
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Daniel Wigh
- Innovation
Centre in Digital Molecular Technologies, Yusuf Hamied Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United
Kingdom
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Mohammed I. Jeraal
- Cambridge
Centre for Advanced Research and Education in Singapore Ltd., 1 Create Way, CREATE Tower #05-05, 138602, Singapore
| | - Rachel Grainger
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom
| | - Gianni Chessari
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom
| | - Christopher N. Johnson
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom
| | - Alexei A. Lapkin
- Innovation
Centre in Digital Molecular Technologies, Yusuf Hamied Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United
Kingdom
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore Ltd., 1 Create Way, CREATE Tower #05-05, 138602, Singapore
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4
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Rabitz H, Russell B, Ho TS. The Surprising Ease of Finding Optimal Solutions for Controlling Nonlinear Phenomena in Quantum and Classical Complex Systems. J Phys Chem A 2023; 127:4224-4236. [PMID: 37142303 DOI: 10.1021/acs.jpca.3c01896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
This Perspective addresses the often observed surprising ease of achieving optimal control of nonlinear phenomena in quantum and classical complex systems. The circumstances involved are wide-ranging, with scenarios including manipulation of atomic scale processes, maximization of chemical and material properties or synthesis yields, Nature's optimization of species' populations by natural selection, and directed evolution. Natural evolution will mainly be discussed in terms of laboratory experiments with microorganisms, and the field is also distinct from the other domains where a scientist specifies the goal(s) and oversees the control process. We use the word "control" in reference to all of the available variables, regardless of the circumstance. The empirical observations on the ease of achieving at least good, if not excellent, control in diverse domains of science raise the question of why this occurs despite the generally inherent complexity of the systems in each scenario. The key to addressing the question lies in examining the associated control landscape, which is defined as the optimization objective as a function of the control variables that can be as diverse as the phenomena under consideration. Controls may range from laser pulses, chemical reagents, chemical processing conditions, out to nucleic acids in the genome and more. This Perspective presents a conjecture, based on present findings, that the systematics of readily finding good outcomes from controlled phenomena may be unified through consideration of control landscapes with the same common set of three underlying assumptions─the existence of an optimal solution, the ability for local movement on the landscape, and the availability of sufficient control resources─whose validity needs assessment in each scenario. In practice, many cases permit using myopic gradient-like algorithms while other circumstances utilize algorithms having some elements of stochasticity or introduced noise, depending on whether the landscape is locally smooth or rough. The overarching observation is that only relatively short searches are required despite the common high dimensionality of the available controls in typical scenarios.
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Affiliation(s)
- Herschel Rabitz
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Benjamin Russell
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Tak-San Ho
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
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5
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Taylor CJ, Pomberger A, Felton KC, Grainger R, Barecka M, Chamberlain TW, Bourne RA, Johnson CN, Lapkin AA. A Brief Introduction to Chemical Reaction Optimization. Chem Rev 2023; 123:3089-3126. [PMID: 36820880 PMCID: PMC10037254 DOI: 10.1021/acs.chemrev.2c00798] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.
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Affiliation(s)
- Connor J Taylor
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, U.K
- Innovation Centre in Digital Molecular Technologies, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Alexander Pomberger
- Innovation Centre in Digital Molecular Technologies, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Kobi C Felton
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
| | - Rachel Grainger
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, U.K
| | - Magda Barecka
- Chemical Engineering Department, Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, United States
- Chemistry and Chemical Biology Department, Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, United States
- Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, 138602 Singapore
| | - Thomas W Chamberlain
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K
| | - Richard A Bourne
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K
| | - Christopher N Johnson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, U.K
| | - Alexei A Lapkin
- Innovation Centre in Digital Molecular Technologies, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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6
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Haas C, Lübbesmeyer M, Jin EH, McDonald MA, Koscher BA, Guimond N, Di Rocco L, Kayser H, Leweke S, Niedenführ S, Nicholls R, Greeves E, Barber DM, Hillenbrand J, Volpin G, Jensen KF. Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening. ACS CENTRAL SCIENCE 2023; 9:307-317. [PMID: 36844498 PMCID: PMC9951288 DOI: 10.1021/acscentsci.2c01042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Indexed: 06/18/2023]
Abstract
Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors' hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC-DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA's data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA's peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.
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Affiliation(s)
- Christian
P. Haas
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Maximilian Lübbesmeyer
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Edward H. Jin
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Matthew A. McDonald
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Brent A. Koscher
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Nicolas Guimond
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany
| | - Laura Di Rocco
- Chemical
& Pharmaceutical Development, Bayer
AG, Pharmaceuticals Division, Müllerstraße 178, 13353 Berlin, Germany
| | - Henning Kayser
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany
| | - Samuel Leweke
- Applied
Mathematics, Bayer AG, Enabling Functions
Division, Kaiser-Wilhelm-Allee
1, 51368 Leverkusen, Germany
| | - Sebastian Niedenführ
- Research
and Development, Computational Life Science, Bayer AG, Crop Science Division, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany
| | - Rachel Nicholls
- Research
and Development, Computational Life Science, Bayer AG, Crop Science Division, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany
| | - Emily Greeves
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - David M. Barber
- Research
and Development, Weed Control Chemistry, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Julius Hillenbrand
- Chemical
& Pharmaceutical Development, Bayer
AG, Pharmaceuticals Division, Friedrich-Ebert-Straße 475, 42117 Wuppertal, Germany
| | - Giulio Volpin
- Research
and Development, Small Molecules Technologies, Bayer AG, Crop Science Division, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Klavs F. Jensen
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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7
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Ruan Y, Lin S, Mo Y. AROPS: A Framework of Automated Reaction Optimization with Parallelized Scheduling. J Chem Inf Model 2023; 63:770-781. [PMID: 36653913 DOI: 10.1021/acs.jcim.2c01168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
With the development of automated experimental platforms and optimization algorithms, chemists can easily optimize chemical reactions in an automated and high-throughput fashion. However, the modules in existing automated experimental platforms are operated in a linear fashion without orchestrating with the optimization algorithm, thus leaving room for further efficiency improvement. Here, we introduced a framework of automated reaction optimization with parallelized scheduling (AROPS) to realize the integration of the optimization algorithm and module scheduling. AROPS relies on a customized Bayesian optimizer to solve multi-reactor/analyzer reaction optimization problems with three different scheduling modes to arrange tasks for various experimental modules. In addition, a mechanism based on probability of improvement (PI) for discarding unpromising ongoing experiments was developed to facilitate freeing up valuable experimental resources in parallelized optimization. We tested the performance of AROPS using a hardware emulator on three representative benchmark reactions encountered in organic synthesis, illustrating that AROPS can trade off optimization time and cost according to the chemists' preference.
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Affiliation(s)
- Yixiang Ruan
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou310027, China.,ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou311215, China
| | - Sen Lin
- Shanghai ChemLex Technology Co., Ltd., Shanghai201210, China
| | - Yiming Mo
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou310027, China.,ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou311215, China
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8
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Zaslavsky J, Bannigan P, Allen C. Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence. Expert Opin Drug Deliv 2023; 20:241-257. [PMID: 36644850 DOI: 10.1080/17425247.2023.2167978] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
INTRODUCTION Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines. AREAS COVERED the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods. EXPERT OPINION The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.
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Affiliation(s)
- Jonathan Zaslavsky
- Leslie Dan Faculty of Pharmacy, University of Toronto, M5S 3M2, Toronto, ON, Canada
| | - Pauric Bannigan
- Leslie Dan Faculty of Pharmacy, University of Toronto, M5S 3M2, Toronto, ON, Canada
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, M5S 3M2, Toronto, ON, Canada
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9
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Clayton AD, Pyzer-Knapp EO, Purdie M, Jones MF, Barthelme A, Pavey J, Kapur N, Chamberlain TW, Blacker AJ, Bourne RA. Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis. Angew Chem Int Ed Engl 2023; 62:e202214511. [PMID: 36346840 PMCID: PMC10108149 DOI: 10.1002/anie.202214511] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/28/2022] [Accepted: 11/08/2022] [Indexed: 11/09/2022]
Abstract
The optimization of multistep chemical syntheses is critical for the rapid development of new pharmaceuticals. However, concatenating individually optimized reactions can lead to inefficient multistep syntheses, owing to chemical interdependencies between the steps. Herein, we develop an automated continuous flow platform for the simultaneous optimization of telescoped reactions. Our approach is applied to a Heck cyclization-deprotection reaction sequence, used in the synthesis of a precursor for 1-methyltetrahydroisoquinoline C5 functionalization. A simple method for multipoint sampling with a single online HPLC instrument was designed, enabling accurate quantification of each reaction, and an in-depth understanding of the reaction pathways. Notably, integration of Bayesian optimization techniques identified an 81 % overall yield in just 14 h, and revealed a favorable competing pathway for formation of the desired product.
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Affiliation(s)
- Adam D Clayton
- Institute of Process Research and Development, Schools of Chemistry & Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | | | - Mark Purdie
- ISEL, Pharmaceutical Technology and Development, Operations, AstraZeneca, Macclesfield, UK
| | - Martin F Jones
- Chemical Development, Pharmaceutical Technology and Development, Operations, AstraZeneca, Macclesfield, UK
| | | | - John Pavey
- UCB Pharma SA, All. de la Recherche 60, 1070, Anderlecht, Belgium
| | - Nikil Kapur
- Institute of Process Research and Development, School of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Thomas W Chamberlain
- Institute of Process Research and Development, Schools of Chemistry & Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - A John Blacker
- Institute of Process Research and Development, Schools of Chemistry & Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Richard A Bourne
- Institute of Process Research and Development, Schools of Chemistry & Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
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10
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McMullen JP, Wyvratt BM. Automated optimization under dynamic flow conditions. REACT CHEM ENG 2023. [DOI: 10.1039/d2re00256f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The combination of feedback optimization with dynamic operations leads to enhanced data-rich experimentation in flow.
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Affiliation(s)
| | - Brian M. Wyvratt
- Merck & Co., Inc., 26 East Lincoln Avenue, Rahway, NJ, 07065, USA
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11
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An Integrated Method of Bayesian Optimization and D-Optimal Design for Chemical Experiment Optimization. Processes (Basel) 2022. [DOI: 10.3390/pr11010087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The smart chemical laboratory has recently emerged as a promising trend for future chemical research, where experiment optimization is of vital importance. The traditional Bayesian optimization (BO) algorithm focuses on exploring the dependent variable space while overlooking the independent variable space. Consequently, the BO algorithm suffers from becoming stuck at local optima, which severely deteriorates the optimization performance, especially with bad-quality initial points. Herein, we propose a novel stochastic framework of Bayesian optimization with D-optimal design (BODO) by integrating BO with D-optimal design. BODO can balance the exploitation in the dependent variable space and the exploration in the independent variable space. We highlight the excellent performance of BODO even with poor initial points on the benchmark alpine2 function. Meanwhile, BODO demonstrates a better average objective function value than BO on the benchmark Summit SnAr chemical process, showing its advantage in chemical experiment optimization and potential application in future chemical experiments.
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12
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Zahrt AF, Mo Y, Nandiwale KY, Shprints R, Heid E, Jensen KF. Machine-Learning-Guided Discovery of Electrochemical Reactions. J Am Chem Soc 2022; 144:22599-22610. [PMID: 36459170 DOI: 10.1021/jacs.2c08997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.
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Affiliation(s)
- Andrew F Zahrt
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02142, United States
| | - Yiming Mo
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02142, United States.,College of Chemical and Biological Engineering, Zhejiang University, Hangzhou310027, China
| | - Kakasaheb Y Nandiwale
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02142, United States
| | - Ron Shprints
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02142, United States
| | - Esther Heid
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02142, United States.,Institute of Materials Chemistry, TU Wien, Vienna1060, Austria
| | - Klavs F Jensen
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02142, United States
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13
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Tamtaji M, Guo X, Tyagi A, Galligan PR, Liu Z, Roxas A, Liu H, Cai Y, Wong H, Zeng L, Xie J, Du Y, Hu Z, Lu D, Goddard WA, Zhu Y, Luo Z. Machine Learning-Aided Design of Gold Core-Shell Nanocatalysts toward Enhanced and Selective Photooxygenation. ACS APPLIED MATERIALS & INTERFACES 2022; 14:46471-46480. [PMID: 36197146 DOI: 10.1021/acsami.2c11101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We demonstrate the use of the machine learning (ML) tools to rapidly and accurately predict the electric field as a guide for designing core-shell Au-silica nanoparticles to enhance 1O2 sensitization and selectivity of organic synthesis. Based on the feature importance analysis, obtained from a deep neural network algorithm, we found a general and linear dependent descriptor (θ ∝ aD0.25t-1, where a, D, and t are the shape constant, size of metal nanoparticles, and distance from the metal surface) for the electric field around the core-shell plasmonic nanoparticle. Directed by the new descriptor, we synthesized gold-silica nanoparticles and validated their plasmonic intensity using scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) mapping. The nanoparticles with θ = 0.40 demonstrate an ∼3-fold increase in the reaction rate of photooxygenation of anthracene and 4% increase in the selectivity of photooxygenation of dihydroartemisinic acid (DHAA), a long-standing goal in organic synthesis. In addition, the combination of ML and experimental investigations shows the synergetic effect of plasmonic enhancement and fluorescence quenching, leading to enhancement for 1O2 generation. Our results from time-dependent density functional theory (TD-DFT) calculations suggest that the presence of an electric field can favor intersystem crossing (ISC) of methylene blue to enhance 1O2 generation. The strategy reported here provides a data-driven catalyst preparation method that can significantly reduce experimental cost while paving the way for designing photocatalysts for organic drug synthesis.
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Affiliation(s)
- Mohsen Tamtaji
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
| | - Xuyun Guo
- Department of Applied Physics, Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hong Kong, China
| | - Abhishek Tyagi
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
| | - Patrick Ryan Galligan
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
| | - Zhenjing Liu
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
| | - Alexander Roxas
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
| | - Hongwei Liu
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
| | - Yuting Cai
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
| | - Hoilun Wong
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
| | - Lun Zeng
- Guangzhou Baiyun Medical Adhesive Co. Ltd., Guangzhou, Guangdong510405, P. R. China
| | - Jianbo Xie
- Guangzhou Baiyun Medical Adhesive Co. Ltd., Guangzhou, Guangdong510405, P. R. China
| | - Yucong Du
- Guangzhou Baiyun Medical Adhesive Co. Ltd., Guangzhou, Guangdong510405, P. R. China
| | - Zhigang Hu
- Silver Age Engineering Plastics (Dongguan) Co. Ltd., Dongguan, Guangdong523187, P. R. China
| | - Dong Lu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, Guangdong511458, P. R. China
| | - William A Goddard
- Materials and Process Simulation Center (MSC), MC 139-74, California Institute of Technology, Pasadena, California91125, United States
| | - Ye Zhu
- Department of Applied Physics, Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zhengtang Luo
- Department of Chemical and Biological Engineering, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, William Mong Institute of Nano Science and Technology, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong999077, P. R. China
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14
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van Putten R, Eyke NS, Baumgartner LM, Schultz VL, Filonenko GA, Jensen KF, Pidko EA. Automation and Microfluidics for the Efficient, Fast, and Focused Reaction Development of Asymmetric Hydrogenation Catalysis. CHEMSUSCHEM 2022; 15:e202200333. [PMID: 35470567 PMCID: PMC9401021 DOI: 10.1002/cssc.202200333] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/17/2022] [Indexed: 06/14/2023]
Abstract
Automation and microfluidic tools potentially enable efficient, fast, and focused reaction development of complex chemistries, while minimizing resource- and material consumption. The introduction of automation-assisted workflows will contribute to the more sustainable development and scale-up of new and improved catalytic technologies. Herein, the application of automation and microfluidics to the development of a complex asymmetric hydrogenation reaction is described. Screening and optimization experiments were performed using an automated microfluidic platform, which enabled a drastic reduction in the material consumption compared to conventional laboratory practices. A suitable catalytic system was identified from a library of RuII -diamino precatalysts. In situ precatalyst activation was studied with 1 H/31 P nuclear magnetic resonance (NMR), and the reaction was scaled up to multigram quantities in a batch autoclave. These reactions were monitored using an automated liquid-phase sampling system. Ultimately, in less than a week of total experimental time, multigram quantities of the target enantiopure alcohol product were provided by this automation-assisted approach.
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Affiliation(s)
- Robbert van Putten
- Inorganic Systems EngineeringDepartment of Chemical EngineeringDelft University of TechnologyVan der Maasweg 92629HZDelftNetherlands
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Natalie S. Eyke
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Lorenz M. Baumgartner
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Victor L. Schultz
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Georgy A. Filonenko
- Inorganic Systems EngineeringDepartment of Chemical EngineeringDelft University of TechnologyVan der Maasweg 92629HZDelftNetherlands
| | - Klavs F. Jensen
- Department of Chemical EngineeringMassachusetts Institute of Technology77 Massachusetts Avenue02139CambridgeMassachusettsUnited States
| | - Evgeny A. Pidko
- Inorganic Systems EngineeringDepartment of Chemical EngineeringDelft University of TechnologyVan der Maasweg 92629HZDelftNetherlands
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15
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Park S, Han H, Kim H, Choi S. Machine Learning Applications for Chemical Reactions. Chem Asian J 2022; 17:e202200203. [PMID: 35471772 PMCID: PMC9401034 DOI: 10.1002/asia.202200203] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/26/2022] [Indexed: 11/30/2022]
Abstract
Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction problems that require huge costs to solve with the existing experimental and simulation methods. In this review, starting with basic representations of chemical reactions, we summarized recent achievements of ML studies on two different problems; predicting reaction properties and synthetic routes. The various ML models are used to predict physical properties related to chemical reaction properties (e. g. thermodynamic changes, activation barriers, and reaction rates). Furthermore, the predictions of reactivity, self-optimization of reaction, and designing retrosynthetic reaction paths are also tackled by ML approaches. Herein we illustrate various ML strategies utilized in the various context of chemical reaction studies.
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Affiliation(s)
- Sanggil Park
- Department of ChemistryIncheon Natoinal University and Research Institute of Basic SciencesIncheon22012Republic of Korea
| | - Herim Han
- Digital Bio R&D CenterMediazenSeoul07789Republic of Korea
- Department of Polymer Science and EngineeringDankook UniversityYongin, Gyeonggi16890Republic of Korea
| | - Hyungjun Kim
- Department of ChemistryIncheon Natoinal University and Research Institute of Basic SciencesIncheon22012Republic of Korea
| | - Sunghwan Choi
- Division of National SupercomputingKorea Institute of Science and Technology InformationDaejeon34141Republic of Korea
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16
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17
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18
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Konan KE, Abollé A, Barré E, Aka EC, Coeffard V, Felpin FX. Developing flow photo-thiol–ene functionalizations of cinchona alkaloids with an autonomous self-optimizing flow reactor. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00509j] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Continuous flow photo-thiol–ene reactions on cinchona alkaloids with a variety of organic thiols have been developed using enabling technologies such as a self-optimizing flow photochemical reactor.
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Affiliation(s)
- Kouakou Eric Konan
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
- Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, Université Nangui Abrogoua, 02 BP 801 Abidjan 02, Côte d'Ivoire
| | - Abollé Abollé
- Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, Université Nangui Abrogoua, 02 BP 801 Abidjan 02, Côte d'Ivoire
| | - Elvina Barré
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
| | - Ehu Camille Aka
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
- Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, Université Nangui Abrogoua, 02 BP 801 Abidjan 02, Côte d'Ivoire
| | - Vincent Coeffard
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
| | - François-Xavier Felpin
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
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19
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Nandiwale KY, Hart T, Zahrt AF, Nambiar AMK, Mahesh PT, Mo Y, Nieves-Remacha MJ, Johnson MD, García-Losada P, Mateos C, Rincón JA, Jensen KF. Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids. REACT CHEM ENG 2022. [DOI: 10.1039/d2re00054g] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Research-scale fully automated flow platform for reaction self-optimization with solids handling facilitates identification of optimal conditions for continuous manufacturing of pharmaceuticals while reducing amounts of raw materials consumed.
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Affiliation(s)
- Kakasaheb Y. Nandiwale
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Travis Hart
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Andrew F. Zahrt
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Anirudh M. K. Nambiar
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Prajwal T. Mahesh
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Yiming Mo
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | | | - Martin D. Johnson
- Small Molecule Design and Development, Eli Lilly and Company, Indianapolis, Indiana 46285, USA
| | - Pablo García-Losada
- Centro de Investigación Lilly S.A., Avda. de la Industria 30, Alcobendas-Madrid 28108, Spain
| | - Carlos Mateos
- Centro de Investigación Lilly S.A., Avda. de la Industria 30, Alcobendas-Madrid 28108, Spain
| | - Juan A. Rincón
- Centro de Investigación Lilly S.A., Avda. de la Industria 30, Alcobendas-Madrid 28108, Spain
| | - Klavs F. Jensen
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
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20
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Knoll S, Jusner CE, Sagmeister P, Williams JD, Hone CA, Horn M, Kappe CO. Autonomous model-based experimental design for rapid reaction development. REACT CHEM ENG 2022. [DOI: 10.1039/d2re00208f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
To automate and democratize model-based experimental design for flow chemistry applications, we report the development of open-source software, Optipus. Reaction models are built in an iterative and automated fashion, for rapid reaction development.
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Affiliation(s)
- Sebastian Knoll
- Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, 8010 Graz, Austria
| | - Clemens E. Jusner
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Peter Sagmeister
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Jason D. Williams
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Christopher A. Hone
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Martin Horn
- Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, 8010 Graz, Austria
| | - C. Oliver Kappe
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
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21
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Gong Y, Xue D, Chuai G, Yu J, Liu Q. DeepReac+: deep active learning for quantitative modeling of organic chemical reactions. Chem Sci 2021; 12:14459-14472. [PMID: 34880997 PMCID: PMC8580052 DOI: 10.1039/d1sc02087k] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 10/08/2021] [Indexed: 11/21/2022] Open
Abstract
Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac.
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Affiliation(s)
- Yukang Gong
- Department of Ophthalmology, Shanghai Tenth People's Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University Shanghai 200072 China
| | - Dongyu Xue
- Department of Ophthalmology, Shanghai Tenth People's Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University Shanghai 200072 China
| | - Guohui Chuai
- Department of Ophthalmology, Shanghai Tenth People's Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University Shanghai 200072 China
| | - Jing Yu
- Department of Ophthalmology, Shanghai Tenth People's Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University Shanghai 200072 China
| | - Qi Liu
- Department of Ophthalmology, Shanghai Tenth People's Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University Shanghai 200072 China
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22
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Hammer AS, Leonov AI, Bell NL, Cronin L. Chemputation and the Standardization of Chemical Informatics. JACS AU 2021; 1:1572-1587. [PMID: 34723260 PMCID: PMC8549037 DOI: 10.1021/jacsau.1c00303] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Indexed: 05/11/2023]
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.
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23
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Webb EW, Scott PJH. Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering. PET Clin 2021; 16:525-532. [PMID: 34537128 DOI: 10.1016/j.cpet.2021.06.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic. In this perspective, the authors offer insights into how the technology could be applied to improve the radiosynthesis of new radiopharmaceuticals for PET imaging, including identification of an optimal labeling approach as well as strategies for radiolabeling reaction optimization.
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Affiliation(s)
- E William Webb
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter J H Scott
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
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24
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Christensen M, Yunker LPE, Adedeji F, Häse F, Roch LM, Gensch T, dos Passos Gomes G, Zepel T, Sigman MS, Aspuru-Guzik A, Hein JE. Data-science driven autonomous process optimization. Commun Chem 2021; 4:112. [PMID: 36697524 PMCID: PMC9814253 DOI: 10.1038/s42004-021-00550-x] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 07/14/2021] [Indexed: 01/28/2023] Open
Abstract
Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.
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Affiliation(s)
- Melodie Christensen
- grid.17091.3e0000 0001 2288 9830Department of Chemistry, University of British Columbia, Vancouver, BC Canada ,grid.417993.10000 0001 2260 0793Department of Process Research and Development, Merck & Co., Inc., Rahway, NJ USA
| | - Lars P. E. Yunker
- grid.17091.3e0000 0001 2288 9830Department of Chemistry, University of British Columbia, Vancouver, BC Canada
| | - Folarin Adedeji
- grid.417993.10000 0001 2260 0793Department of Process Research and Development, Merck & Co., Inc., Rahway, NJ USA
| | - Florian Häse
- grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA ,grid.17063.330000 0001 2157 2938Department of Chemistry, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Computer Science, University of Toronto, Toronto, ON Canada ,grid.494618.6Vector Institute for Artificial Intelligence, Toronto, ON Canada ,ChemOS Sàrl, Lausanne, Vaud Switzerland
| | - Loïc M. Roch
- grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA ,grid.17063.330000 0001 2157 2938Department of Chemistry, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Computer Science, University of Toronto, Toronto, ON Canada ,ChemOS Sàrl, Lausanne, Vaud Switzerland
| | - Tobias Gensch
- grid.223827.e0000 0001 2193 0096Department of Chemistry, University of Utah, Salt Lake City, UT USA
| | - Gabriel dos Passos Gomes
- grid.17063.330000 0001 2157 2938Department of Chemistry, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Computer Science, University of Toronto, Toronto, ON Canada ,grid.494618.6Vector Institute for Artificial Intelligence, Toronto, ON Canada
| | - Tara Zepel
- grid.17091.3e0000 0001 2288 9830Department of Chemistry, University of British Columbia, Vancouver, BC Canada
| | - Matthew S. Sigman
- grid.223827.e0000 0001 2193 0096Department of Chemistry, University of Utah, Salt Lake City, UT USA
| | - Alán Aspuru-Guzik
- grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA ,grid.17063.330000 0001 2157 2938Department of Chemistry, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Computer Science, University of Toronto, Toronto, ON Canada ,grid.494618.6Vector Institute for Artificial Intelligence, Toronto, ON Canada ,grid.440050.50000 0004 0408 2525Canadian Institute for Advanced Research, Toronto, ON Canada
| | - Jason E. Hein
- grid.17091.3e0000 0001 2288 9830Department of Chemistry, University of British Columbia, Vancouver, BC Canada
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25
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Hall BL, Taylor CJ, Labes R, Massey AF, Menzel R, Bourne RA, Chamberlain TW. Autonomous optimisation of a nanoparticle catalysed reduction reaction in continuous flow. Chem Commun (Camb) 2021; 57:4926-4929. [PMID: 33870978 DOI: 10.1039/d1cc00859e] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
An automated continuous flow reactor system equipped with inline analysis, was developed and applied in the self-optimisation of a nanoparticle catalysed reaction. The system was used to optimise the experimental conditions of a gold nanoparticle catalysed 4-nitrophenol reduction reaction, towards maximum conversion in under 2.5 hours. The data obtained from this optimisation was then used to generate a kinetic model, allowing us to predict the outcome of the reaction under different conditions. By combining continuous flow nanoparticle synthesis with this approach, the development timeline for these emerging catalysts could be significantly accelerated.
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Affiliation(s)
- Brendan L Hall
- Institute for Process Research and Development, School of Chemistry, University of Leeds, Leeds, LS2 9JT, UK.
| | - Connor J Taylor
- Institute for Process Research and Development, School of Chemistry, University of Leeds, Leeds, LS2 9JT, UK.
| | - Ricardo Labes
- Institute for Process Research and Development, School of Chemistry, University of Leeds, Leeds, LS2 9JT, UK.
| | - Alexander F Massey
- Institute for Process Research and Development, School of Chemistry, University of Leeds, Leeds, LS2 9JT, UK.
| | - Robert Menzel
- Institute for Process Research and Development, School of Chemistry, University of Leeds, Leeds, LS2 9JT, UK.
| | - Richard A Bourne
- Institute for Process Research and Development, School of Chemistry, University of Leeds, Leeds, LS2 9JT, UK.
| | - Thomas W Chamberlain
- Institute for Process Research and Development, School of Chemistry, University of Leeds, Leeds, LS2 9JT, UK.
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26
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Breen CP, Nambiar AM, Jamison TF, Jensen KF. Ready, Set, Flow! Automated Continuous Synthesis and Optimization. TRENDS IN CHEMISTRY 2021. [DOI: 10.1016/j.trechm.2021.02.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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27
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Cao L, Russo D, Lapkin AA. Automated robotic platforms in design and development of formulations. AIChE J 2021. [DOI: 10.1002/aic.17248] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Liwei Cao
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore
| | - Danilo Russo
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore
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28
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Florit F, Nambiar AMK, Breen CP, Jamison TF, Jensen KF. Design of dynamic trajectories for efficient and data-rich exploration of flow reaction design spaces. REACT CHEM ENG 2021. [DOI: 10.1039/d1re00350j] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Sinusoidal variations of operative parameters in flow chemistry allows the fast exploration of chemical design spaces through inline measurements of an objective function.
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Affiliation(s)
- Federico Florit
- Massachusetts Institute of Technology, Department of Chemical Engineering, Cambridge, MA 02139, USA
- Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta”, Piazza Leonardo da Vinci, 32, 20133, Milano, Italy
| | - Anirudh M. K. Nambiar
- Massachusetts Institute of Technology, Department of Chemical Engineering, Cambridge, MA 02139, USA
| | - Christopher P. Breen
- Massachusetts Institute of Technology, Department of Chemistry, Cambridge, MA 02139, USA
| | - Timothy F. Jamison
- Massachusetts Institute of Technology, Department of Chemistry, Cambridge, MA 02139, USA
| | - Klavs F. Jensen
- Massachusetts Institute of Technology, Department of Chemical Engineering, Cambridge, MA 02139, USA
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29
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Zhang C, Amar Y, Cao L, Lapkin AA. Solvent Selection for Mitsunobu Reaction Driven by an Active Learning Surrogate Model. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00376] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Chonghuan Zhang
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Yehia Amar
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Liwei Cao
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd., 1 CREATE Way, CREATE Tower #05-05, 138602 Singapore
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd., 1 CREATE Way, CREATE Tower #05-05, 138602 Singapore
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30
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Vasudevan N, Wimmer E, Barré E, Cortés‐Borda D, Rodriguez‐Zubiri M, Felpin F. Direct C−H Arylation of Indole‐3‐Acetic Acid Derivatives Enabled by an Autonomous Self‐Optimizing Flow Reactor. Adv Synth Catal 2020. [DOI: 10.1002/adsc.202001217] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- N. Vasudevan
- Université de Nantes CNRS CEISAM UMR 6230 2 rue de la Houssinière 44322 Nantes France
| | - Eric Wimmer
- Université de Nantes CNRS CEISAM UMR 6230 2 rue de la Houssinière 44322 Nantes France
| | - Elvina Barré
- Université de Nantes CNRS CEISAM UMR 6230 2 rue de la Houssinière 44322 Nantes France
| | - Daniel Cortés‐Borda
- Université de Nantes CNRS CEISAM UMR 6230 2 rue de la Houssinière 44322 Nantes France
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31
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Mo Y, Rughoobur G, Nambiar AMK, Zhang K, Jensen KF. A Multifunctional Microfluidic Platform for High‐Throughput Experimentation of Electroorganic Chemistry. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.202009819] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Yiming Mo
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Girish Rughoobur
- Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Anirudh M. K. Nambiar
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Kara Zhang
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
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32
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Mo Y, Rughoobur G, Nambiar AMK, Zhang K, Jensen KF. A Multifunctional Microfluidic Platform for High‐Throughput Experimentation of Electroorganic Chemistry. Angew Chem Int Ed Engl 2020; 59:20890-20894. [DOI: 10.1002/anie.202009819] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Indexed: 01/11/2023]
Affiliation(s)
- Yiming Mo
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Girish Rughoobur
- Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Anirudh M. K. Nambiar
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Kara Zhang
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
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33
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Coley CW, Eyke NS, Jensen KF. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angew Chem Int Ed Engl 2020; 59:22858-22893. [DOI: 10.1002/anie.201909987] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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34
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Coley CW, Eyke NS, Jensen KF. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201909987] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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35
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Sui J, Yan J, Liu D, Wang K, Luo G. Continuous Synthesis of Nanocrystals via Flow Chemistry Technology. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1902828. [PMID: 31755221 DOI: 10.1002/smll.201902828] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 10/11/2019] [Indexed: 05/28/2023]
Abstract
Modern nanotechnologies bring humanity to a new age, and advanced methods for preparing functional nanocrystals are cornerstones. A considerable variety of nanomaterials has been created over the past decades, but few were prepared on the macro scale, even fewer making it to the stage of industrial production. The gap between academic research and engineering production is expected to be filled by flow chemistry technology, which relies on microreactors. Microreaction devices and technologies for synthesizing different kinds of nanocrystals are discussed from an engineering point of view. The advantages of microreactors, the important features of flow chemistry systems, and methods to apply them in the syntheses of salt, oxide, metal, alloy, and quantum dot nanomaterials are summarized. To further exhibit the scaling-up of nanocrystal synthesis, recent reports on using microreactors with gram per hour and larger production rates are highlighted. Finally, an industrial example for preparing 10 tons of CaCO3 nanoparticles per day is introduced, which shows the great potential for flow chemistry processes to transfer lab research to industry.
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Affiliation(s)
- Jinsong Sui
- The State Key Lab of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Junyu Yan
- The State Key Lab of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Di Liu
- The State Key Lab of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Kai Wang
- The State Key Lab of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Guangsheng Luo
- The State Key Lab of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
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36
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Su S, Yang Y, Gan H, Zheng S, Gu F, Zhao C, Xu J. Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism. J Chem Inf Model 2020; 60:1165-1174. [DOI: 10.1021/acs.jcim.9b00929] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Shimin Su
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Yuyao Yang
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Hanlin Gan
- Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education, School of Chemistry and Environment, South China Normal University, South Circle at University City, Guangzhou 510006, China
| | - Shuangjia Zheng
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Fenglong Gu
- Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education, School of Chemistry and Environment, South China Normal University, South Circle at University City, Guangzhou 510006, China
| | - Chao Zhao
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Jun Xu
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
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37
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Woodley JM. Advances in biological conversion technologies: new opportunities for reaction engineering. REACT CHEM ENG 2020. [DOI: 10.1039/c9re00422j] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Reaction engineering needs to embrace biological conversion technologies, on the road to identify more sustainable routes for chemical manufacture.
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Affiliation(s)
- John M. Woodley
- Department of Chemical and Biochemical Engineering
- Technical University of Denmark (DTU)
- DK-2800 Kgs. Lyngby
- Denmark
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38
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Ebikade EO, Wang Y, Samulewicz N, Hasa B, Vlachos D. Active learning-driven quantitative synthesis–structure–property relations for improving performance and revealing active sites of nitrogen-doped carbon for the hydrogen evolution reaction. REACT CHEM ENG 2020. [DOI: 10.1039/d0re00243g] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A data-driven quantitative synthesis–structure–property relation methodology to elucidate correlations between catalyst synthesis conditions, structural properties and observed performance, providing a systematic way to optimize practical catalysts.
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Affiliation(s)
- Elvis Osamudiamhen Ebikade
- Catalysis Center for Energy Innovation
- University of Delaware
- Newark
- USA
- Department of Chemical and Biomolecular Engineering
| | - Yifan Wang
- Catalysis Center for Energy Innovation
- University of Delaware
- Newark
- USA
- Department of Chemical and Biomolecular Engineering
| | - Nicholas Samulewicz
- Catalysis Center for Energy Innovation
- University of Delaware
- Newark
- USA
- Department of Chemical and Biomolecular Engineering
| | - Bjorn Hasa
- Catalysis Center for Energy Innovation
- University of Delaware
- Newark
- USA
| | - Dionisios Vlachos
- Catalysis Center for Energy Innovation
- University of Delaware
- Newark
- USA
- Department of Chemical and Biomolecular Engineering
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39
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Eyke NS, Green WH, Jensen KF. Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening. REACT CHEM ENG 2020. [DOI: 10.1039/d0re00232a] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Through iterative selection of maximally informative experiments, active learning renders exhaustive screening obsolete. Chosen experiments are used to train models that are accurate over the entire domain, thus reducing the experiment burden.
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Affiliation(s)
- Natalie S. Eyke
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
| | - William H. Green
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
| | - Klavs F. Jensen
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
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40
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Ładosz A, Kuhnle C, Jensen KF. Characterization of reaction enthalpy and kinetics in a microscale flow platform. REACT CHEM ENG 2020. [DOI: 10.1039/d0re00304b] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
We report an isothermal flow calorimeter for characterization of reaction enthalpy and kinetics.
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Affiliation(s)
- Agnieszka Ładosz
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
| | - Christina Kuhnle
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
| | - Klavs F. Jensen
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
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41
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Fu Z, Li X, Wang Z, Li Z, Liu X, Wu X, Zhao J, Ding X, Wan X, Zhong F, Wang D, Luo X, Chen K, Liu H, Wang J, Jiang H, Zheng M. Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki–Miyaura cross-coupling reaction. Org Chem Front 2020. [DOI: 10.1039/d0qo00544d] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Deep learning was used to optimize chemical reactions with the quantum mechanical properties of chemical contexts and reaction conditions as inputs. The trained deep learning model determines optimal reaction conditions by in silico exploration of accessible reaction space.
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42
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Bennett JA, Campbell ZS, Abolhasani M. Role of continuous flow processes in green manufacturing of pharmaceuticals and specialty chemicals. Curr Opin Chem Eng 2019. [DOI: 10.1016/j.coche.2019.07.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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43
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Aka EC, Wimmer E, Barré E, Vasudevan N, Cortés-Borda D, Ekou T, Ekou L, Rodriguez-Zubiri M, Felpin FX. Reconfigurable Flow Platform for Automated Reagent Screening and Autonomous Optimization for Bioinspired Lignans Synthesis. J Org Chem 2019; 84:14101-14112. [PMID: 31568728 DOI: 10.1021/acs.joc.9b02263] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Naturally occurring benzoxanthenones, which belong to the vast family of lignans, are promising biologically relevant targets. They are biosynthetically produced by the oxidative dimerization of 2-propenyl phenols. In this manuscript, we disclose a powerful automated flow-based strategy for identifying and optimizing a cobalt-catalyzed oxidizing system for the bioinspired dimerization of 2-propenyl phenols. We designed a reconfigurable flow reactor associating online monitoring and process control instrumentation. Our machine was first configured as an automated screening platform to evaluate a matrix of 4 catalysts (plus the blank) and 5 oxidants (plus the blank) at two different temperatures, resulting in an array of 50 reactions. The automated screening was conducted on micromole scale at a rate of one fully characterized reaction every 26 min. After having identified the most promising cobalt-catalyzed oxidizing system, the automated screening platform was straightforwardly reconfigured to an autonomous self-optimizing flow reactor by implementation of an optimization algorithm in the closed-loop system. The optimization campaign allowed the determination of very effective experimental conditions in a limited number of experiments, which allowed us to prepare the natural products carpanone and polemannone B as well as synthetic analogues.
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Affiliation(s)
- Ehu Camille Aka
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Eric Wimmer
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Elvina Barré
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Natarajan Vasudevan
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Daniel Cortés-Borda
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Tchirioua Ekou
- Université Nangui Abrogoua , Laboratoire de Thermodynamique et de Physico-Chimie du Milieu , 02 BP 801 Abidjan 02 , Côte d'Ivoire
| | - Lynda Ekou
- Université Nangui Abrogoua , Laboratoire de Thermodynamique et de Physico-Chimie du Milieu , 02 BP 801 Abidjan 02 , Côte d'Ivoire
| | - Mireia Rodriguez-Zubiri
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - François-Xavier Felpin
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
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44
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Fujinami M, Seino J, Nukazawa T, Ishida S, Iwamoto T, Nakai H. Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables. CHEM LETT 2019. [DOI: 10.1246/cl.190267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Mikito Fujinami
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Junji Seino
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Takumi Nukazawa
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Shintaro Ishida
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Takeaki Iwamoto
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Hiromi Nakai
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Element Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Katsura, Kyoto 615-8520, Japan
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45
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Baumgartner LM, Dennis JM, White NA, Buchwald SL, Jensen KF. Use of a Droplet Platform To Optimize Pd-Catalyzed C–N Coupling Reactions Promoted by Organic Bases. Org Process Res Dev 2019. [DOI: 10.1021/acs.oprd.9b00236] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Lorenz M. Baumgartner
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Joseph M. Dennis
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Nicholas A. White
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Stephen L. Buchwald
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Klavs F. Jensen
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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46
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Amar Y, Schweidtmann AM, Deutsch P, Cao L, Lapkin A. Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis. Chem Sci 2019; 10:6697-6706. [PMID: 31367324 PMCID: PMC6625492 DOI: 10.1039/c9sc01844a] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 05/28/2019] [Indexed: 12/19/2022] Open
Abstract
Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives - high conversion and high diastereomeric excess - the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.
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Affiliation(s)
- Yehia Amar
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
| | - Artur M Schweidtmann
- Aachener Verfahrenstechnik - Process Systems Engineering , RWTH Aachen University , Aachen , Germany
| | - Paul Deutsch
- UCB Pharma S.A. Allée de la Recherche , 60 1070 , Brussels , Belgium
| | - Liwei Cao
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
- Cambridge Centre for Advanced Research and Education in Singapore Ltd. , 1 Create Way, CREATE Tower #05-05 , 138602 , Singapore
| | - Alexei Lapkin
- Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .
- Cambridge Centre for Advanced Research and Education in Singapore Ltd. , 1 Create Way, CREATE Tower #05-05 , 138602 , Singapore
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47
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Isbrandt ES, Sullivan RJ, Newman SG. High Throughput Strategies for the Discovery and Optimization of Catalytic Reactions. Angew Chem Int Ed Engl 2019; 58:7180-7191. [DOI: 10.1002/anie.201812534] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Indexed: 12/29/2022]
Affiliation(s)
- Eric S. Isbrandt
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Ryan J. Sullivan
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Stephen G. Newman
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
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48
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Haas CP, Tallarek U. Kinetics Studies on a Multicomponent Knoevenagel-Michael Domino Reaction by an Automated Flow Reactor. ChemistryOpen 2019; 8:606-614. [PMID: 31110932 PMCID: PMC6511915 DOI: 10.1002/open.201900124] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Indexed: 12/13/2022] Open
Abstract
The optimization of complex chemical reaction systems is often a troublesome and time‐consuming process. The application of modern technologies, including automated reactors and analytics, opens the avenue for generating large data sets on chemical reaction processes in a short period of time. In this work, an automated flow reactor is used to present detailed kinetics and mechanistic studies about an amine‐catalyzed Knoevenagel−Michael domino reaction to yield tetrahydrochromene derivatives. High‐performance monoliths as catalyst supports and online coupled HPLC analysis allow for time‐efficient data generation. We show that the two‐step multicomponent domino reaction does not follow the kinetics of consecutive reaction steps proceeding independently from each other. Instead, the starting materials of both individual reactions compete for the active sites on the heterogeneous catalyst, which lowers the rate constants of both steps. This knowledge was used to implement a more efficient experimental setup which increased the turnover numbers of the catalyst, without adjusting common reaction parameters like temperature, reaction time, and concentrations.
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Affiliation(s)
- Christian P Haas
- Department of Chemistry Philipps-Universität Marburg Hans-Meerwein-Strasse 4 D-35032 Marburg Germany
| | - Ulrich Tallarek
- Department of Chemistry Philipps-Universität Marburg Hans-Meerwein-Strasse 4 D-35032 Marburg Germany
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49
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Isbrandt ES, Sullivan RJ, Newman SG. Hochdurchsatzstrategien zur Entdeckung und Optimierung katalytischer Reaktionen. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201812534] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Eric S. Isbrandt
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Kanada
| | - Ryan J. Sullivan
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Kanada
| | - Stephen G. Newman
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Kanada
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50
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Wimmer E, Cortés-Borda D, Brochard S, Barré E, Truchet C, Felpin FX. An autonomous self-optimizing flow machine for the synthesis of pyridine–oxazoline (PyOX) ligands. REACT CHEM ENG 2019. [DOI: 10.1039/c9re00096h] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A continuous flow synthesis of PyOX ligands has been developed using an autonomous self-optimizing flow reactor.
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Affiliation(s)
- Eric Wimmer
- Université de Nantes
- CNRS UMR 6230
- CEISAM
- 44322 Nantes Cedex 3
- France
| | | | - Solène Brochard
- Université de Nantes
- CNRS UMR 6230
- CEISAM
- 44322 Nantes Cedex 3
- France
| | - Elvina Barré
- Université de Nantes
- CNRS UMR 6230
- CEISAM
- 44322 Nantes Cedex 3
- France
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