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Dalton DM, Walroth RC, Rouget-Virbel C, Mack KA, Toste FD. Utopia Point Bayesian Optimization Finds Condition-Dependent Selectivity for N-Methyl Pyrazole Condensation. J Am Chem Soc 2024; 146:15779-15786. [PMID: 38804885 PMCID: PMC11177315 DOI: 10.1021/jacs.4c01616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024]
Abstract
Utopia Point Bayesian Optimization (UPBO) was used to identify reaction conditions that are highly selective for the formation of N1 and N2-methyl-3-aryl pyrazole constitutional isomers. UPBO was used to explore a wide chemical space and identify basic reaction conditions for a typically acid-catalyzed Knorr pyrazole condensation. These studies revealed that selectivity in the reaction stems from a condition-dependent equilibrium of intermediates prior to dehydration. For the N2-methyl isomer reaction pathway, a hemiaminal intermediate was found to form reversibly under the reaction conditions, enabling a highly selective synthesis of the N2 isomer upon dehydrative workup. UPBO was able to successfully optimize conversion and selectivity simultaneously with search spaces of >1 million potential variable combinations without the need for high-performance computational resources.
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Affiliation(s)
- Derek M. Dalton
- Department
of Synthetic Molecule Process Chemistry, Genentech, Inc., South
San Francisco, California 94080, United States
| | - Richard C. Walroth
- Department
of Synthetic Molecule Process Chemistry, Genentech, Inc., South
San Francisco, California 94080, United States
| | - Caroline Rouget-Virbel
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
| | - Kyle A. Mack
- Department
of Synthetic Molecule Process Chemistry, Genentech, Inc., South
San Francisco, California 94080, United States
| | - F. Dean Toste
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
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2
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Schilter O, Gutierrez DP, Folkmann LM, Castrogiovanni A, García-Durán A, Zipoli F, Roch LM, Laino T. Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes. Chem Sci 2024; 15:7732-7741. [PMID: 38784737 PMCID: PMC11110165 DOI: 10.1039/d3sc05607d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024] Open
Abstract
Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of an integrated platform; we conducted simultaneous optimizations of four different terminal alkynes and two reaction routes using an automation platform combined with a Bayesian optimization platform. Remarkably, we achieved a conversion rate of over 80% for all four substrates in 23 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to identify the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.
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Affiliation(s)
- Oliver Schilter
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
| | | | - Linnea M Folkmann
- Atinary Technologies Route de la Corniche 4 1066 Epalinges Switzerland
| | | | | | - Federico Zipoli
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
| | - Loïc M Roch
- Atinary Technologies Route de la Corniche 4 1066 Epalinges Switzerland
| | - Teodoro Laino
- IBM Research Europe Säumerstrasse 4 8803 Rüschlikon Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis) Switzerland
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3
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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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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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Colliandre L, Muller C. Bayesian Optimization in Drug Discovery. Methods Mol Biol 2024; 2716:101-136. [PMID: 37702937 DOI: 10.1007/978-1-0716-3449-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Drug discovery deals with the search for initial hits and their optimization toward a targeted clinical profile. Throughout the discovery pipeline, the candidate profile will evolve, but the optimization will mainly stay a trial-and-error approach. Tons of in silico methods have been developed to improve and fasten this pipeline. Bayesian optimization (BO) is a well-known method for the determination of the global optimum of a function. In the last decade, BO has gained popularity in the early drug design phase. This chapter starts with the concept of black box optimization applied to drug design and presents some approaches to tackle it. Then it focuses on BO and explains its principle and all the algorithmic building blocks needed to implement it. This explanation aims to be accessible to people involved in drug discovery projects. A strong emphasis is made on the solutions to deal with the specific constraints of drug discovery. Finally, a large set of practical applications of BO is highlighted.
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6
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Capaldo L, Wen Z, Noël T. A field guide to flow chemistry for synthetic organic chemists. Chem Sci 2023; 14:4230-4247. [PMID: 37123197 PMCID: PMC10132167 DOI: 10.1039/d3sc00992k] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 03/15/2023] [Indexed: 03/17/2023] Open
Abstract
Flow chemistry has unlocked a world of possibilities for the synthetic community, but the idea that it is a mysterious "black box" needs to go. In this review, we show that several of the benefits of microreactor technology can be exploited to push the boundaries in organic synthesis and to unleash unique reactivity and selectivity. By "lifting the veil" on some of the governing principles behind the observed trends, we hope that this review will serve as a useful field guide for those interested in diving into flow chemistry.
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Affiliation(s)
- Luca Capaldo
- Flow Chemistry Group, Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam 1098 XH Amsterdam The Netherlands
| | - Zhenghui Wen
- Flow Chemistry Group, Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam 1098 XH Amsterdam The Netherlands
| | - Timothy Noël
- Flow Chemistry Group, Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam 1098 XH Amsterdam The Netherlands
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7
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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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