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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Wagner F, Sagmeister P, Jusner CE, Tampone TG, Manee V, Buono FG, Williams JD, Kappe CO. A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308034. [PMID: 38273711 PMCID: PMC10987115 DOI: 10.1002/advs.202308034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/21/2023] [Indexed: 01/27/2024]
Abstract
Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.
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Affiliation(s)
- Florian Wagner
- Center for Continuous Flow Synthesis and Processing (CC FLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
| | - Peter Sagmeister
- Center for Continuous Flow Synthesis and Processing (CC FLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
| | - Clemens E. Jusner
- Center for Continuous Flow Synthesis and Processing (CC FLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
| | - Thomas G. Tampone
- Boehringer Ingelheim Pharmaceuticals, Inc900 Ridgebury RoadRidgefieldCT06877USA
| | - Vidhyadhar Manee
- Boehringer Ingelheim Pharmaceuticals, Inc900 Ridgebury RoadRidgefieldCT06877USA
| | - Frederic G. Buono
- Boehringer Ingelheim Pharmaceuticals, Inc900 Ridgebury RoadRidgefieldCT06877USA
| | - Jason D. Williams
- Center for Continuous Flow Synthesis and Processing (CC FLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
| | - C. Oliver Kappe
- Center for Continuous Flow Synthesis and Processing (CC FLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
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3
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Wang JY, Stevens JM, Kariofillis SK, Tom MJ, Golden DL, Li J, Tabora JE, Parasram M, Shields BJ, Primer DN, Hao B, Del Valle D, DiSomma S, Furman A, Zipp GG, Melnikov S, Paulson J, Doyle AG. Identifying general reaction conditions by bandit optimization. Nature 2024; 626:1025-1033. [PMID: 38418912 DOI: 10.1038/s41586-024-07021-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/03/2024] [Indexed: 03/02/2024]
Abstract
Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.
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Affiliation(s)
- Jason Y Wang
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
| | - Jason M Stevens
- Chemical Process Development, Bristol Myers Squibb, Summit, NJ, USA
| | - Stavros K Kariofillis
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - Mai-Jan Tom
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
| | - Dung L Golden
- Chemical Process Development, Bristol Myers Squibb, Summit, NJ, USA
| | - Jun Li
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Jose E Tabora
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Marvin Parasram
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Department of Chemistry, New York University, New York, NY, USA
| | - Benjamin J Shields
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Molecular Structure and Design, Bristol Myers Squibb, Cambridge, MA, USA
| | - David N Primer
- Chemical Process Development, Bristol Myers Squibb, Summit, NJ, USA
- Loxo Oncology at Lilly, Louisville, CO, USA
| | - Bo Hao
- Janssen Research and Development, Spring House, PA, USA
| | - David Del Valle
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Stacey DiSomma
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Ariel Furman
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - G Greg Zipp
- Discovery Synthesis, Bristol Myers Squibb, Princeton, NJ, USA
| | | | - James Paulson
- Chemical Process Development, Bristol Myers Squibb, New Brunswick, NJ, USA
| | - Abigail G Doyle
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA.
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4
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Slattery A, Wen Z, Tenblad P, Sanjosé-Orduna J, Pintossi D, den Hartog T, Noël T. Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science 2024; 383:eadj1817. [PMID: 38271529 DOI: 10.1126/science.adj1817] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 12/13/2023] [Indexed: 01/27/2024]
Abstract
The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in a manufacturing environment geared primarily toward thermal chemistry. In this work, we present a versatile flow-based robotic platform to address these challenges through the integration of readily available hardware and custom software. Our open-source platform combines a liquid handler, syringe pumps, a tunable continuous-flow photoreactor, inexpensive Internet of Things devices, and an in-line benchtop nuclear magnetic resonance spectrometer to enable automated, data-rich optimization with a closed-loop Bayesian optimization strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise to easily monitor, analyze, and improve photocatalytic reactions with respect to both continuous and discrete variables. The system's effectiveness was demonstrated by increasing overall reaction yields and improving space-time yields compared with those of previously reported processes.
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Affiliation(s)
- Aidan Slattery
- Flow Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
| | - Zhenghui Wen
- Flow Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
| | - Pauline Tenblad
- Flow Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
| | - Jesús Sanjosé-Orduna
- Flow Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
| | - Diego Pintossi
- Flow Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
| | - Tim den Hartog
- Flow Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
- Zuyd University of Applied Sciences, Nieuw Eyckholt 300, 6419 DJ Heerlen, Netherlands
- Netherlands Organisation for Applied Scientific Research (TNO), High Tech Campus 25, 5656 AE Eindhoven, Netherlands
| | - Timothy Noël
- Flow Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
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5
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Bai J, Mosbach S, Taylor CJ, Karan D, Lee KF, Rihm SD, Akroyd J, Lapkin AA, Kraft M. A dynamic knowledge graph approach to distributed self-driving laboratories. Nat Commun 2024; 15:462. [PMID: 38263405 PMCID: PMC10805810 DOI: 10.1038/s41467-023-44599-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.
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Affiliation(s)
- Jiaru Bai
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
| | - Sebastian Mosbach
- 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 (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Connor J Taylor
- Astex Pharmaceuticals, 436 Cambridge Science Park Milton Road, Cambridge, CB4 0QA, UK
- Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- Faculty of Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Dogancan Karan
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Kok Foong Lee
- CMCL Innovations, Sheraton House, Cambridge, CB3 0AX, UK
| | - Simon D Rihm
- 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 (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Jethro Akroyd
- 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 (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Alexei A 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 (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
- Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Markus Kraft
- 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 (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore.
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore, Singapore.
- The Alan Turing Institute, London, NW1 2DB, UK.
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6
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Sadeghi S, Bateni F, Kim T, Son DY, Bennett JA, Orouji N, Punati VS, Stark C, Cerra TD, Awad R, Delgado-Licona F, Xu J, Mukhin N, Dickerson H, Reyes KG, Abolhasani M. Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab. NANOSCALE 2024; 16:580-591. [PMID: 38116636 DOI: 10.1039/d3nr05034c] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Lead-based metal halide perovskite (MHP) nanocrystals (NCs) have emerged as a promising class of semiconducting nanomaterials for a wide range of optoelectronic and photoelectronic applications. However, the intrinsic lead toxicity of MHP NCs has significantly hampered their large-scale device applications. Copper-base MHP NCs with composition-tunable optical properties have emerged as a prominent lead-free MHP NC candidate. However, comprehensive synthesis space exploration, development, and synthesis science studies of copper-based MHP NCs have been limited by the manual nature of flask-based synthesis and characterization methods. In this study, we present an autonomous approach for the development of lead-free MHP NCs via seamless integration of a modular microfluidic platform with machine learning-assisted NC synthesis modeling and experiment selection to establish a self-driving fluidic lab for accelerated NC synthesis science studies. For the first time, a successful and reproducible in-flow synthesis of Cs3Cu2I5 NCs is presented. Autonomous experimentation is then employed for rapid in-flow synthesis science studies of Cs3Cu2I5 NCs. The autonomously generated experimental NC synthesis dataset is then utilized for fast-tracked synthetic route optimization of high-performing Cs3Cu2I5 NCs.
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Affiliation(s)
- Sina Sadeghi
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Fazel Bateni
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Taekhoon Kim
- Synthesis Technical Unit, Material Research Center, Samsung Advanced Institute of Technology, SEC, 130, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Dae Yong Son
- Synthesis Technical Unit, Material Research Center, Samsung Advanced Institute of Technology, SEC, 130, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Jeffrey A Bennett
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Negin Orouji
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Venkat S Punati
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Christine Stark
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Teagan D Cerra
- Department of Physics, Weber State University, Ogden, UT 84408, USA
| | - Rami Awad
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Fernando Delgado-Licona
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Jinge Xu
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Nikolai Mukhin
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Hannah Dickerson
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Kristofer G Reyes
- Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USA
| | - Milad Abolhasani
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
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7
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Szymanski NJ, Nevatia P, Bartel CJ, Zeng Y, Ceder G. Autonomous and dynamic precursor selection for solid-state materials synthesis. Nat Commun 2023; 14:6956. [PMID: 37907493 PMCID: PMC10618174 DOI: 10.1038/s41467-023-42329-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/06/2023] [Indexed: 11/02/2023] Open
Abstract
Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS3) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material's formation. Based on this information, ARROWS3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.
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Affiliation(s)
- Nathan J Szymanski
- Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA, 94720, USA
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Pragnay Nevatia
- Department of Chemical Engineering, UC Berkeley, Berkeley, CA, 94720, USA
| | - Christopher J Bartel
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Yan Zeng
- Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA, 94720, USA.
| | - Gerbrand Ceder
- Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA, 94720, USA.
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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8
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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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9
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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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [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.
| | | | - 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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10
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Mottafegh A, Ahn GN, Kim DP. Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis. LAB ON A CHIP 2023; 23:1613-1621. [PMID: 36722393 DOI: 10.1039/d2lc00938b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Optimizing a wide range of reaction parameters, steps, and pathways is currently considered one of the most complex and challenging problems in microflow-based organic synthesis. As a novel solution, Bayesian optimization (BO) has been utilized to efficiently guide the optimized conditions of flow reactors; however, the benchmarking process for selecting the optimal model among various surrogate models remains inefficient. In this work, we report meta optimization (MO) by benchmarking multiple surrogate models in real-time without any pre-work, which is realized by evaluating the expected values obtained by the regressor used to build each surrogate model, enabling efficient optimization of reaction conditions. By the comparison of the performance of MO with that of various BOs on four datasets of different flow syntheses, it was verified that MO consistently performs the best-in-class for all emulators developed through machine learning, while the conventional BOs based on surrogate models such as the Gaussian process, random forest, neural network ensemble, and gradient boosting demonstrated varying performances from each emulator, which implies that benchmarking is required.
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Affiliation(s)
- Amirreza Mottafegh
- Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
| | - Gwang-Noh Ahn
- Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
| | - Dong-Pyo Kim
- Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
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11
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Abstract
How do you get into flow? We trained in flow chemistry during postdoctoral research and are now applying it in new areas: materials chemistry, crystallization, and supramolecular synthesis. Typically, when researchers think of "flow", they are considering predominantly liquid-based organic synthesis; application to other disciplines comes with its own challenges. In this Perspective, we highlight why we use and champion flow technologies in our fields, summarize some of the questions we encounter when discussing entry into flow research, and suggest steps to make the transition into the field, emphasizing that communication and collaboration between disciplines is key.
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Affiliation(s)
- Andrea Laybourn
- Faculty
of Engineering, University of Nottingham, University Park Campus, Nottingham NG7 2RD, U.K.
| | - Karen Robertson
- Faculty
of Engineering, University of Nottingham, University Park Campus, Nottingham NG7 2RD, U.K.
| | - Anna G. Slater
- Department
of Chemistry and Materials Innovation Factory, University of Liverpool, Crown Street, Liverpool L69 7ZD, U.K.
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12
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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] [MESH Headings] [Grants] [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 DevelopmentSchools of Chemistry & Chemical and Process EngineeringUniversity of LeedsLeedsLS2 9JTUK
| | | | - Mark Purdie
- ISELPharmaceutical Technology and Development, OperationsAstraZenecaMacclesfieldUK
| | - Martin F. Jones
- Chemical DevelopmentPharmaceutical Technology and Development, OperationsAstraZenecaMacclesfieldUK
| | | | - John Pavey
- UCB Pharma SAAll. de la Recherche 601070AnderlechtBelgium
| | - Nikil Kapur
- Institute of Process Research and DevelopmentSchool of Mechanical EngineeringUniversity of LeedsLeedsLS2 9JTUK
| | - Thomas W. Chamberlain
- Institute of Process Research and DevelopmentSchools of Chemistry & Chemical and Process EngineeringUniversity of LeedsLeedsLS2 9JTUK
| | - A. John Blacker
- Institute of Process Research and DevelopmentSchools of Chemistry & Chemical and Process EngineeringUniversity of LeedsLeedsLS2 9JTUK
| | - Richard A. Bourne
- Institute of Process Research and DevelopmentSchools of Chemistry & Chemical and Process EngineeringUniversity of LeedsLeedsLS2 9JTUK
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13
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Davies JC, Pattison D, Hirst JD. Machine learning for yield prediction for chemical reactions using in situ sensors. J Mol Graph Model 2023; 118:108356. [PMID: 36272195 DOI: 10.1016/j.jmgm.2022.108356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 11/28/2022]
Abstract
Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reaction probe has 12 sensors to measure properties of interest, including temperature, pressure, and colour. Colour was a good predictor of product formation for this reaction and machine learning models were able to learn which of the properties were important. Predictions for the current product formation (in terms of % yield) had a mean absolute error of 1.2%. For predicting 30, 60 and 120 min ahead the error rose to 3.4, 4.1 and 4.6%, respectively. The work here presents an example into the insight that can be obtained from applying machine learning methods to sensor data in synthetic chemistry.
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Affiliation(s)
- Joseph C Davies
- School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | | | - Jonathan D Hirst
- School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
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14
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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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15
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Angello NH, Rathore V, Beker W, Wołos A, Jira ER, Roszak R, Wu TC, Schroeder CM, Aspuru-Guzik A, Grzybowski BA, Burke MD. Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling. Science 2022; 378:399-405. [DOI: 10.1126/science.adc8743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging and consequential problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches. This study provides a practical road map for solving multidimensional chemical optimization problems with large search spaces.
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Affiliation(s)
- Nicholas H. Angello
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Vandana Rathore
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Agnieszka Wołos
- Allchemy, Inc., Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Edward R. Jira
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rafał Roszak
- Allchemy, Inc., Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Tony C. Wu
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Charles M. Schroeder
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Canadian Institute for Advanced Research, Toronto, ON, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Bartosz A. Grzybowski
- Allchemy, Inc., Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
- Center for Soft and Living Matter, Institute for Basic Science, Ulsan, Republic of Korea
- Department of Chemistry, Ulsan Institute of Science and Technology, Ulsan, Republic of Korea
| | - Martin D. Burke
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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16
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Nambiar AK, Breen CP, Hart T, Kulesza T, Jamison TF, Jensen KF. Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform. ACS CENTRAL SCIENCE 2022; 8:825-836. [PMID: 35756374 PMCID: PMC9228554 DOI: 10.1021/acscentsci.2c00207] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Indexed: 06/15/2023]
Abstract
Computer-aided synthesis planning (CASP) tools can propose retrosynthetic pathways and forward reaction conditions for the synthesis of organic compounds, but the limited availability of context-specific data currently necessitates experimental development to fully specify process details. We plan and optimize a CASP-proposed and human-refined multistep synthesis route toward an exemplary small molecule, sonidegib, on a modular, robotic flow synthesis platform with integrated process analytical technology (PAT) for data-rich experimentation. Human insights address catalyst deactivation and improve yield by strategic choices of order of addition. Multi-objective Bayesian optimization identifies optimal values for categorical and continuous process variables in the multistep route involving 3 reactions (including heterogeneous hydrogenation) and 1 separation. The platform's modularity, robotic reconfigurability, and flexibility for convergent synthesis are shown to be essential for allowing variation of downstream residence time in multistep flow processes and controlling the order of addition to minimize undesired reactivity. Overall, the work demonstrates how automation, machine learning, and robotics enhance manual experimentation through assistance with idea generation, experimental design, execution, and optimization.
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Affiliation(s)
- Anirudh
M. K. Nambiar
- Department
of Chemical Engineering, Massachusetts Institute
of Technology,77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Christopher P. Breen
- Department
of Chemistry, Massachusetts Institute of
Technology,77 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Travis Hart
- Department
of Chemical Engineering, Massachusetts Institute
of Technology,77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Timothy Kulesza
- Department
of Chemical Engineering, Massachusetts Institute
of Technology,77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Timothy F. Jamison
- Department
of Chemistry, Massachusetts Institute of
Technology,77 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Klavs F. Jensen
- Department
of Chemical Engineering, Massachusetts Institute
of Technology,77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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17
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18
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Sagmeister P, Ort FF, Jusner CE, Hebrault D, Tampone T, Buono FG, Williams JD, Kappe CO. Autonomous Multi-Step and Multi-Objective Optimization Facilitated by Real-Time Process Analytics. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105547. [PMID: 35106974 PMCID: PMC8981902 DOI: 10.1002/advs.202105547] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/12/2022] [Indexed: 05/04/2023]
Abstract
Autonomous flow reactors are becoming increasingly utilized in the synthesis of organic compounds, yet the complexity of the chemical reactions and analytical methods remains limited. The development of a modular platform which uses rapid flow NMR and FTIR measurements, combined with chemometric modeling, is presented for efficient and timely analysis of reaction outcomes. This platform is tested with a four variable single-step reaction (nucleophilic aromatic substitution), to determine the most effective optimization methodology. The self-optimization approach with minimal background knowledge proves to provide the optimal reaction parameters within the shortest operational time. The chosen approach is then applied to a seven variable two-step optimization problem (imine formation and cyclization), for the synthesis of the active pharmaceutical ingredient edaravone. Despite the exponentially increased complexity of this optimization problem, the platform achieves excellent results in a relatively small number of iterations, leading to >95% solution yield of the intermediate and up to 5.42 kg L-1 h-1 space-time yield for this pharmaceutically relevant product.
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Affiliation(s)
- Peter Sagmeister
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
| | - Florian F. Ort
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
| | - Clemens E. Jusner
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
| | - Dominique Hebrault
- Chemical Development USBoehringer Ingelheim Pharmaceuticals, Inc.900 Ridgebury RoadRidgefieldConnecticut06877USA
| | - Thomas Tampone
- Chemical Development USBoehringer Ingelheim Pharmaceuticals, Inc.900 Ridgebury RoadRidgefieldConnecticut06877USA
| | - Frederic G. Buono
- Chemical Development USBoehringer Ingelheim Pharmaceuticals, Inc.900 Ridgebury RoadRidgefieldConnecticut06877USA
| | - Jason D. Williams
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
| | - C. Oliver Kappe
- Institute of ChemistryUniversity of GrazNAWI Graz, Heinrichstrasse 28Graz8010Austria
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 13Graz8010Austria
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19
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Rincón JA, Navarro A, Nieves-Remacha MJ, Eugenio de Diego J, Ruble JC, de la Puente ML, Trigo MJ, Schaefer BA. Hybrid Flow-Batch Model for the Efficient Synthesis of 2-(Dimethylamino)-6-methylpyridin-4-ol. Org Process Res Dev 2022. [DOI: 10.1021/acs.oprd.1c00264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Juan A. Rincón
- Centro de Investigación Lilly S.A.U., Alcobendas-Madrid 28108, Spain
| | - Antonio Navarro
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | | | | | - J. Craig Ruble
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | | | - María Jesús Trigo
- Centro de Investigación Lilly S.A.U., Alcobendas-Madrid 28108, Spain
| | - Brian A. Schaefer
- Eastman Chemical Company, 200 S. Wilcox Drive, Kingsport, Tennessee 37660, United States
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20
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Bai J, Cao L, Mosbach S, Akroyd J, Lapkin AA, Kraft M. From Platform to Knowledge Graph: Evolution of Laboratory Automation. JACS AU 2022; 2:292-309. [PMID: 35252980 PMCID: PMC8889618 DOI: 10.1021/jacsau.1c00438] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Indexed: 05/19/2023]
Abstract
High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.
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Affiliation(s)
- Jiaru Bai
- 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
| | - Sebastian Mosbach
- 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), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Jethro Akroyd
- 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), CREATE Tower #05-05, 1 Create Way, 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), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Markus Kraft
- 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), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459 Singapore
- The
Alan Turing Institute, London NW1 2DB, United Kingdom
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21
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22
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Simon K, Sagmeister P, Munday RH, Leslie K, Hone CA, Kappe CO. Automated Flow and Real-Time Analytics Approach for Screening Functional Group Tolerance in Heterogeneous Catalytic Reactions. Catal Sci Technol 2022. [DOI: 10.1039/d2cy00059h] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Heterogeneous hydrogenation reactions are widely used in synthesis, and performing them using continuous flow technologies addresses many of the safety, scalability and sustainability issues. However, one of the main potential...
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23
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Mueller P, Clayton AD, Manson J, Riley S, May O, Govan N, Notman S, Ley SV, Chamberlain TW, Bourne R. Automated Multi-Objective Reaction Optimisation: Which Algorithm Should I Use? REACT CHEM ENG 2022. [DOI: 10.1039/d1re00549a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Multi-objective optimisation algorithms (MOOAs) are, of increasing interest for the efficient optimisation of chemical processes. However, an algorithms performance can vary on a case-by-case basis, depending on the complexity of...
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24
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Ke J, Gao C, Folgueiras-Amador AA, Jolley KE, de Frutos O, Mateos C, Rincón JA, Brown RCD, Poliakoff M, George MW. Self-Optimization of Continuous Flow Electrochemical Synthesis Using Fourier Transform Infrared Spectroscopy and Gas Chromatography. APPLIED SPECTROSCOPY 2022; 76:38-50. [PMID: 34911387 DOI: 10.1177/00037028211059848] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A continuous-flow electrochemical synthesis platform has been developed to enable self-optimization of reaction conditions of organic electrochemical reactions using attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) and gas chromatography (GC) as online real-time monitoring techniques. We have overcome the challenges in using ATR FT-IR as the downstream analytical methods imposed when a large amount of hydrogen gas is produced from the counter electrode by designing two types of gas-liquid separators (GLS) for analysis of the product mixture flowing from the electrochemical reactor. In particular, we report an integrated GLS with an ATR FT-IR probe at the reactor outlet to give a facile and low-cost solution to determining the concentrations of products in gas-liquid two-phase flow. This approach provides a reliable method for quantifying low-volatile analytes, which can be problematic to be monitored by GC. Two electrochemical reactions the methoxylation of 1-formylpyrrolidine and the oxidation of 3-bromobenzyl alcohol were investigated to demonstrate that the optimal conditions can be located within the pre-defined multi-dimensional reaction parameter spaces without intervention of the operator by using the stable noisy optimization by branch and FIT (SNOBFIT) algorithm.
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Affiliation(s)
- Jie Ke
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
| | - Chuang Gao
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
- Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, China
| | | | - Katherine E Jolley
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
- School of Chemistry, University of Southampton, Southampton, UK
| | - Oscar de Frutos
- Centro de Investigación Lilly S.A., Alcobendas-Madrid, Spain
| | - Carlos Mateos
- Centro de Investigación Lilly S.A., Alcobendas-Madrid, Spain
| | - Juan A Rincón
- Centro de Investigación Lilly S.A., Alcobendas-Madrid, Spain
| | | | - Martyn Poliakoff
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
| | - Michael W George
- School of Chemistry, 6123University of Nottingham, Nottingham, UK
- Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, China
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25
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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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26
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Liang R, Duan X, Zhang J, Yuan Z. Bayesian based reaction optimization for complex continuous gas–liquid–solid reactions. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00397f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
In recent years, self-optimization strategies have been gradually utilized for the determination of optimal reaction conditions owing to their high convenience and independence from researchers' experience.
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Affiliation(s)
- Runzhe Liang
- State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Xiaonan Duan
- State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Jisong Zhang
- State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Zhihong Yuan
- State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
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27
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Crandall Z, Basemann K, Qi L, Windus TL. Rxn Rover: automation of chemical reactions with user-friendly, modular software. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00265a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Automation of chemical reactions through tools such as Rxn Rover in research and development is an enabling technology to reduce cost and waste management in technology transformations towards renewable feedstocks and energy in the chemical industry.
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Affiliation(s)
- Zachery Crandall
- U.S. DOE Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Kevin Basemann
- U.S. DOE Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Long Qi
- U.S. DOE Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Theresa L. Windus
- U.S. DOE Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
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28
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Knox ST, Parkinson SJ, Wilding CYP, Bourne R, Warren NJ. Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation. Polym Chem 2022. [DOI: 10.1039/d2py00040g] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Application of artificial intelligence and machine learning for polymer discovery offers an opportunity to meet the drastic need for the next generation high performing and sustainable polymer materials. Here, these...
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29
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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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30
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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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31
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SBA15-supported nano-ruthenium catalyst for the oxidative cleavage of alkenes to aldehydes under flow conditions. Tetrahedron Lett 2021. [DOI: 10.1016/j.tetlet.2021.153509] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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32
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Ollerton K, Greenaway RL, Slater AG. Enabling Technology for Supramolecular Chemistry. Front Chem 2021; 9:774987. [PMID: 34869224 PMCID: PMC8634592 DOI: 10.3389/fchem.2021.774987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/21/2021] [Indexed: 11/13/2022] Open
Abstract
Supramolecular materials-materials that exploit non-covalent interactions-are increasing in structural complexity, selectivity, function, stability, and scalability, but their use in applications has been comparatively limited. In this Minireview, we summarize the opportunities presented by enabling technology-flow chemistry, high-throughput screening, and automation-to wield greater control over the processes in supramolecular chemistry and accelerate the discovery and use of self-assembled systems. Finally, we give an outlook for how these tools could transform the future of the field.
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Affiliation(s)
- Katie Ollerton
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, United Kingdom
| | - Rebecca L. Greenaway
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London, United Kingdom
| | - Anna G. Slater
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, United Kingdom
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33
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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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34
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Bonner A, Loftus A, Padgham AC, Baumann M. Forgotten and forbidden chemical reactions revitalised through continuous flow technology. Org Biomol Chem 2021; 19:7737-7753. [PMID: 34549240 DOI: 10.1039/d1ob01452h] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Continuous flow technology has played an undeniable role in enabling modern chemical synthesis, whereby a myriad of reactions can now be performed with greater efficiency, safety and control. As flow chemistry furthermore delivers more sustainable and readily scalable routes to important target structures a growing number of industrial applications are being reported. In this review we highlight the impact of flow chemistry on revitalising important chemical reactions that were either forgotten soon after their initial report as necessary improvements were not realised due to a lack of available technology, or forbidden due to unacceptable safety concerns relating to the experimental procedure. In both cases flow processing in combination with further reaction optimisation has rendered a powerful set of tools that make such transformations not only highly efficient but moreover very desirable due to a more streamlined construction of desired scaffolds. This short review highlights important contributions from academic and industrial laboratories predominantly from the last 5 years allowing the reader to gain an appreciation of the impact of flow chemistry.
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Affiliation(s)
- Arlene Bonner
- School of Chemistry, University College Dublin, Science Centre South, D04 N2E5, Dublin, Ireland.
| | - Aisling Loftus
- School of Chemistry, University College Dublin, Science Centre South, D04 N2E5, Dublin, Ireland.
| | - Alex C Padgham
- School of Chemistry, University College Dublin, Science Centre South, D04 N2E5, Dublin, Ireland.
| | - Marcus Baumann
- School of Chemistry, University College Dublin, Science Centre South, D04 N2E5, Dublin, Ireland.
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35
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Morin MA, Zhang W(P, Mallik D, Organ MG. Sampling and Analysis in Flow: The Keys to Smarter, More Controllable, and Sustainable Fine‐Chemical Manufacturing. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202102009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Mathieu A. Morin
- Department of Chemistry and Biomolecular Sciences Centre for Catalysis Research and Innovation (CCRI) University of Ottawa 10 Marie Curie Ottawa ON K1N 6N5 Canada
- Department of Chemistry Carleton University 203 Steacie Building, 1125 Colonel By Drive Ottawa ON K1S 5B6 Canada
| | - Wenyao (Peter) Zhang
- Department of Chemistry York University 4700 Keele Street Toronto ON M3J 1P3 Canada
| | - Debasis Mallik
- Department of Chemistry and Biomolecular Sciences Centre for Catalysis Research and Innovation (CCRI) University of Ottawa 10 Marie Curie Ottawa ON K1N 6N5 Canada
| | - Michael G. Organ
- Department of Chemistry and Biomolecular Sciences Centre for Catalysis Research and Innovation (CCRI) University of Ottawa 10 Marie Curie Ottawa ON K1N 6N5 Canada
- Department of Chemistry York University 4700 Keele Street Toronto ON M3J 1P3 Canada
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36
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Morin MA, Zhang WP, Mallik D, Organ MG. Sampling and Analysis in Flow: The Keys to Smarter, More Controllable, and Sustainable Fine-Chemical Manufacturing. Angew Chem Int Ed Engl 2021; 60:20606-20626. [PMID: 33811800 DOI: 10.1002/anie.202102009] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/23/2021] [Indexed: 11/08/2022]
Abstract
Process analytical technology (PAT) is a system designed to help chemists better understand and control manufacturing processes. PAT systems operate through the combination of analytical devices, reactor control elements, and mathematical models to ensure the quality of the final product through a quality by design (QbD) approach. The expansion of continuous manufacturing in the pharmaceutical and fine-chemical industry requires the development of PAT tools suitable for continuous operation in the environment of flow reactors. This requires innovative approaches to sampling and analysis from flowing media to maintain the integrity of the reactor content and the analyte of interest. The following Review discusses examples of PAT tools implemented in flow chemistry for the preparation of small organic molecules, and applications of self-optimization tools.
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Affiliation(s)
- Mathieu A Morin
- Department of Chemistry and Biomolecular Sciences, Centre for Catalysis Research and Innovation (CCRI), University of Ottawa, 10 Marie Curie, Ottawa, ON, K1N 6N5, Canada.,Department of Chemistry, Carleton University, 203 Steacie Building, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada
| | - Wenyao Peter Zhang
- Department of Chemistry, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada
| | - Debasis Mallik
- Department of Chemistry and Biomolecular Sciences, Centre for Catalysis Research and Innovation (CCRI), University of Ottawa, 10 Marie Curie, Ottawa, ON, K1N 6N5, Canada
| | - Michael G Organ
- Department of Chemistry and Biomolecular Sciences, Centre for Catalysis Research and Innovation (CCRI), University of Ottawa, 10 Marie Curie, Ottawa, ON, K1N 6N5, Canada.,Department of Chemistry, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada
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37
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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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38
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Szymanski NJ, Zeng Y, Huo H, Bartel CJ, Kim H, Ceder G. Toward autonomous design and synthesis of novel inorganic materials. MATERIALS HORIZONS 2021; 8:2169-2198. [PMID: 34846423 DOI: 10.1039/d1mh00495f] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.
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Affiliation(s)
- Nathan J Szymanski
- Department of Materials Science & Engineering, UC Berkeley, Berkeley, CA 94720, USA.
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39
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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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40
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Taylor CJ, Seki H, Dannheim FM, Willis MJ, Clemens G, Taylor BA, Chamberlain TW, Bourne RA. An automated computational approach to kinetic model discrimination and parameter estimation. REACT CHEM ENG 2021; 6:1404-1411. [PMID: 34354841 PMCID: PMC8315272 DOI: 10.1039/d1re00098e] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/07/2021] [Indexed: 01/15/2023]
Abstract
We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc.) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source. We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification.![]()
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Affiliation(s)
- Connor J Taylor
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds Leeds LS2 9JT UK
| | - Hikaru Seki
- Department of Chemistry, University of Cambridge Cambridge CB2 1EW UK
| | | | - Mark J Willis
- School of Engineering, University of Newcastle Newcastle upon Tyne NE1 7RU UK
| | - Graeme Clemens
- Chemical Development, Pharmaceutical Technology & Development, Operations, AstraZeneca Macclesfield UK
| | - Brian A Taylor
- Chemical Development, Pharmaceutical Technology & Development, Operations, AstraZeneca Macclesfield UK
| | - 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 UK
| | - 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 UK
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41
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Sagmeister P, Lebl R, Castillo I, Rehrl J, Kruisz J, Sipek M, Horn M, Sacher S, Cantillo D, Williams JD, Kappe CO. Advanced Real-Time Process Analytics for Multistep Synthesis in Continuous Flow*. Angew Chem Int Ed Engl 2021; 60:8139-8148. [PMID: 33433918 PMCID: PMC8048486 DOI: 10.1002/anie.202016007] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Indexed: 12/28/2022]
Abstract
In multistep continuous flow chemistry, studying complex reaction mixtures in real time is a significant challenge, but provides an opportunity to enhance reaction understanding and control. We report the integration of four complementary process analytical technology tools (NMR, UV/Vis, IR and UHPLC) in the multistep synthesis of an active pharmaceutical ingredient, mesalazine. This synthetic route exploits flow processing for nitration, high temperature hydrolysis and hydrogenation reactions, as well as three inline separations. Advanced data analysis models were developed (indirect hard modeling, deep learning and partial least squares regression), to quantify the desired products, intermediates and impurities in real time, at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating both steady state and dynamic experiments and represents a significant step forward in data-driven continuous flow synthesis.
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Affiliation(s)
- Peter Sagmeister
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 138010GrazAustria
- Institute of ChemistryUniversity of Graz, NAWI GrazHeinrichstrasse 288010GrazAustria
| | - René Lebl
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 138010GrazAustria
- Institute of ChemistryUniversity of Graz, NAWI GrazHeinrichstrasse 288010GrazAustria
| | - Ismael Castillo
- Institute of Automation and ControlGraz University of TechnologyInffeldgasse 21b8010GrazAustria
| | - Jakob Rehrl
- Research Center Pharmaceutical Engineering (RCPE)Inffeldgasse 138010GrazAustria
| | - Julia Kruisz
- Research Center Pharmaceutical Engineering (RCPE)Inffeldgasse 138010GrazAustria
| | - Martin Sipek
- Evon GmbHWollsdorf 1548181St. Ruprecht a. d. RaabAustria
| | - Martin Horn
- Institute of Automation and ControlGraz University of TechnologyInffeldgasse 21b8010GrazAustria
| | - Stephan Sacher
- Research Center Pharmaceutical Engineering (RCPE)Inffeldgasse 138010GrazAustria
| | - David Cantillo
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 138010GrazAustria
- Institute of ChemistryUniversity of Graz, NAWI GrazHeinrichstrasse 288010GrazAustria
| | - Jason D. Williams
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 138010GrazAustria
- Institute of ChemistryUniversity of Graz, NAWI GrazHeinrichstrasse 288010GrazAustria
| | - C. Oliver Kappe
- Center for Continuous Flow Synthesis and Processing (CCFLOW)Research Center Pharmaceutical Engineering GmbH (RCPE)Inffeldgasse 138010GrazAustria
- Institute of ChemistryUniversity of Graz, NAWI GrazHeinrichstrasse 288010GrazAustria
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42
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Greenaway RL, Jelfs KE. Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2004831. [PMID: 33565203 DOI: 10.1002/adma.202004831] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/28/2020] [Indexed: 06/12/2023]
Abstract
Organic materials find application in a range of areas, including optoelectronics, sensing, encapsulation, molecular separations, and photocatalysis. The discovery of materials is frustratingly slow however, particularly when contrasted to the vast chemical space of possibilities based on the near limitless options for organic molecular precursors. The difficulty in predicting the material assembly, and consequent properties, of any molecule is another significant roadblock to targeted materials design. There has been significant progress in the development of computational approaches to screen large numbers of materials, for both their structure and properties, helping guide synthetic researchers toward promising materials. In particular, artificial intelligence techniques have the potential to make significant impact in many elements of the discovery process. Alongside this, automation and robotics are increasing the scale and speed with which materials synthesis can be realized. Herein, the focus is on demonstrating the power of integrating computational and experimental materials discovery programmes, including both a summary of key situations where approaches can be combined and a series of case studies that demonstrate recent successes.
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Affiliation(s)
- Rebecca L Greenaway
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, W12 0BZ, UK
| | - Kim E Jelfs
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, W12 0BZ, UK
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Sagmeister P, Lebl R, Castillo I, Rehrl J, Kruisz J, Sipek M, Horn M, Sacher S, Cantillo D, Williams JD, Kappe CO. Advanced Real‐Time Process Analytics for Multistep Synthesis in Continuous Flow**. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202016007] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Peter Sagmeister
- Center for Continuous Flow 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
| | - René Lebl
- Center for Continuous Flow 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
| | - Ismael Castillo
- Institute of Automation and Control Graz University of Technology Inffeldgasse 21b 8010 Graz Austria
| | - Jakob Rehrl
- Research Center Pharmaceutical Engineering (RCPE) Inffeldgasse 13 8010 Graz Austria
| | - Julia Kruisz
- Research Center Pharmaceutical Engineering (RCPE) Inffeldgasse 13 8010 Graz Austria
| | - Martin Sipek
- Evon GmbH Wollsdorf 154 8181 St. Ruprecht a. d. Raab Austria
| | - Martin Horn
- Institute of Automation and Control Graz University of Technology Inffeldgasse 21b 8010 Graz Austria
| | - Stephan Sacher
- Research Center Pharmaceutical Engineering (RCPE) Inffeldgasse 13 8010 Graz Austria
| | - David Cantillo
- Center for Continuous Flow 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 Flow 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
| | - C. Oliver Kappe
- Center for Continuous Flow 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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44
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Shields BJ, Stevens J, Li J, Parasram M, Damani F, Alvarado JIM, Janey JM, Adams RP, Doyle AG. Bayesian reaction optimization as a tool for chemical synthesis. Nature 2021; 590:89-96. [DOI: 10.1038/s41586-021-03213-y] [Citation(s) in RCA: 132] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/11/2020] [Indexed: 02/04/2023]
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Flow Biocatalysis: A Challenging Alternative for the Synthesis of APIs and Natural Compounds. Int J Mol Sci 2021; 22:ijms22030990. [PMID: 33498198 PMCID: PMC7863935 DOI: 10.3390/ijms22030990] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/13/2021] [Accepted: 01/14/2021] [Indexed: 01/01/2023] Open
Abstract
Biocatalysts represent an efficient, highly selective and greener alternative to metal catalysts in both industry and academia. In the last two decades, the interest in biocatalytic transformations has increased due to an urgent need for more sustainable industrial processes that comply with the principles of green chemistry. Thanks to the recent advances in biotechnologies, protein engineering and the Nobel prize awarded concept of direct enzymatic evolution, the synthetic enzymatic toolbox has expanded significantly. In particular, the implementation of biocatalysts in continuous flow systems has attracted much attention, especially from industry. The advantages of flow chemistry enable biosynthesis to overcome well-known limitations of “classic” enzymatic catalysis, such as time-consuming work-ups and enzyme inhibition, as well as difficult scale-up and process intensifications. Moreover, continuous flow biocatalysis provides access to practical, economical and more sustainable synthetic pathways, an important aspect for the future of pharmaceutical companies if they want to compete in the market while complying with European Medicines Agency (EMA), Food and Drug Administration (FDA) and green chemistry requirements. This review focuses on the most recent advances in the use of flow biocatalysis for the synthesis of active pharmaceutical ingredients (APIs), pharmaceuticals and natural products, and the advantages and limitations are discussed.
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Power LA, Clayton AD, Reynolds WR, Hose DRJ, Ainsworth C, Chamberlain TW, Nguyen BN, Bourne RA, Kapur N, Blacker AJ. Selective separation of amines from continuous processes using automated pH controlled extraction. REACT CHEM ENG 2021. [DOI: 10.1039/d1re00205h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
An automated separation system is described for identifying the optimal conditions for purifying an amine from a mixture.
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Affiliation(s)
- Luke A. Power
- Institute of Process Research and Development, School of Chemistry, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Adam D. Clayton
- Institute of Process Research and Development, School of Chemistry, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - William R. Reynolds
- Institute of Process Research and Development, School of Chemistry, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - David R. J. Hose
- Chemical Development, Pharmaceutical Technology and Development, Operations, AstraZeneca, Macclesfield, SK10 2NA, UK
| | - Caroline Ainsworth
- Chemical Development, Pharmaceutical Technology and Development, Operations, AstraZeneca, Macclesfield, SK10 2NA, UK
| | - Thomas W. Chamberlain
- Institute of Process Research and Development, School of Chemistry, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Bao N. Nguyen
- Institute of Process Research and Development, School of Chemistry, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Richard A. Bourne
- Institute of Process Research and Development, School of Chemistry, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Nikil Kapur
- Institute of Process Research and Development, School of Chemistry, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - A. John Blacker
- Institute of Process Research and Development, School of Chemistry, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
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Saib A, Bara-Estaún A, Harper OJ, Berry DBG, Thomlinson IA, Broomfield-Tagg R, Lowe JP, Lyall CL, Hintermair U. Engineering aspects of FlowNMR spectroscopy setups for online analysis of solution-phase processes. REACT CHEM ENG 2021. [DOI: 10.1039/d1re00217a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In this article we review some fundamental engineering concepts and evaluate components and materials required to assemble and operate safe and effective FlowNMR setups that reliably generate meaningful results.
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Affiliation(s)
- Asad Saib
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
| | - Alejandro Bara-Estaún
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
| | - Owen J. Harper
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Centre for Sustainable & Circular Technologies, University of Bath, Bath BA2 7AY, UK
| | - Daniel B. G. Berry
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
| | - Isabel A. Thomlinson
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Centre for Sustainable & Circular Technologies, University of Bath, Bath BA2 7AY, UK
| | - Rachael Broomfield-Tagg
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
| | - John P. Lowe
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
| | - Catherine L. Lyall
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
| | - Ulrich Hintermair
- Department of Chemistry, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Dynamic Reaction Monitoring Facility, University of Bath, Claverton Down, BA2 7AY Bath, UK
- Centre for Sustainable & Circular Technologies, University of Bath, Bath BA2 7AY, UK
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48
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Kim HW, Lee SW, Na GS, Han SJ, Kim SK, Shin JH, Chang H, Kim YT. Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence. REACT CHEM ENG 2021. [DOI: 10.1039/d0re00378f] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Using machine learning and metaheuristic optimization, we optimize the reaction conditions for non-oxidative conversion of methane.
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Affiliation(s)
- Hyun Woo Kim
- Chemical Data-Driven Research Center
- Korea Research Institute of Chemical Technology (KRICT)
- Daejeon 34114
- Korea
| | - Sung Woo Lee
- C1 Gas & Carbon Convergent Research Center
- Korea Research Institute of Chemical Technology
- Daejeon 34114
- Korea
| | - Gyoung S. Na
- Chemical Data-Driven Research Center
- Korea Research Institute of Chemical Technology (KRICT)
- Daejeon 34114
- Korea
| | - Seung Ju Han
- C1 Gas & Carbon Convergent Research Center
- Korea Research Institute of Chemical Technology
- Daejeon 34114
- Korea
| | - Seok Ki Kim
- C1 Gas & Carbon Convergent Research Center
- Korea Research Institute of Chemical Technology
- Daejeon 34114
- Korea
- Advanced Materials and Chemical Engineering
| | - Jung Ho Shin
- Chemical Data-Driven Research Center
- Korea Research Institute of Chemical Technology (KRICT)
- Daejeon 34114
- Korea
| | - Hyunju Chang
- Chemical Data-Driven Research Center
- Korea Research Institute of Chemical Technology (KRICT)
- Daejeon 34114
- Korea
| | - Yong Tae Kim
- C1 Gas & Carbon Convergent Research Center
- Korea Research Institute of Chemical Technology
- Daejeon 34114
- Korea
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49
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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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50
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Liao J, Zhang S, Wang Z, Song X, Zhang D, Kumar R, Jin J, Ren P, You H, Chen FE. Transition-metal catalyzed asymmetric reactions under continuous flow from 2015 to early 2020. GREEN SYNTHESIS AND CATALYSIS 2020. [DOI: 10.1016/j.gresc.2020.08.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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