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Strieth-Kalthoff F, Szymkuć S, Molga K, Aspuru-Guzik A, Glorius F, Grzybowski BA. Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge. J Am Chem Soc 2024. [PMID: 38598363 DOI: 10.1021/jacs.4c00338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of "hybrid" algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.
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Affiliation(s)
- Felix Strieth-Kalthoff
- University of Toronto, Department of Chemistry and Department of Computer Science, 80 St. George St., Toronto, Ontario M5S 3H6, Canada
- University of Toronto, Department of Computer Science, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Sara Szymkuć
- Allchemy, 2145 45th Street #201, Highland, Indiana 46322, United States
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
| | - Karol Molga
- Allchemy, 2145 45th Street #201, Highland, Indiana 46322, United States
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
| | - Alán Aspuru-Guzik
- University of Toronto, Department of Chemistry and Department of Computer Science, 80 St. George St., Toronto, Ontario M5S 3H6, Canada
- University of Toronto, Department of Computer Science, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave., Toronto, Ontario M5G 1M1, Canada
- University of Toronto, Department of Chemical Engineering and Applied Chemistry, 200 College St., Toronto, Ontario M5S 3E5, Canada
- University of Toronto, Department of Materials Science and Engineering, 184 College St., Toronto, Ontario M5S 3E4, Canada
| | - Frank Glorius
- Universität Münster, Organisch-Chemisches Institut, Corrensstr. 36, 48149 Münster, Germany
| | - Bartosz A Grzybowski
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
- IBS Center for Algorithmic and Robotized Synthesis, CARS, UNIST 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, South Korea
- Department of Chemistry, UNIST, 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, South Korea
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2
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Kim H, Lee K, Kim C, Lim J, Kim WY. DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening. J Chem Inf Model 2024; 64:2432-2444. [PMID: 37651152 DOI: 10.1021/acs.jcim.3c01134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address this issue, a few recent studies have attempted to use deep learning models to estimate the synthetic accessibility of many molecules rapidly. However, retrosynthetic analysis tools used to train the models rely on reaction templates automatically extracted from a large reaction database that are not domain-specific and may exhibit low chemical correctness. To overcome this limitation, we introduce DFRscore (Drug-Focused Retrosynthetic score), a deep learning-based approach for a more practical assessment of synthetic accessibility in drug discovery. The DFRscore model is trained exclusively on drug-focused reactions, providing a predicted number of minimally required synthetic steps for each compound. This approach enables practitioners to filter out compounds that do not meet their desired level of synthetic accessibility at an early stage of high-throughput virtual screening for accelerated drug discovery. The proposed strategy can be easily adapted to other domains by adjusting the synthesis planning setup of the reaction templates and starting materials.
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Affiliation(s)
- Hyeongwoo Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Kyunghoon Lee
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Chansu Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Jaechang Lim
- HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea
| | - Woo Youn Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea
- AI Institute, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
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3
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Back S, Aspuru-Guzik A, Ceriotti M, Gryn'ova G, Grzybowski B, Gu GH, Hein J, Hippalgaonkar K, Hormázabal R, Jung Y, Kim S, Kim WY, Moosavi SM, Noh J, Park C, Schrier J, Schwaller P, Tsuda K, Vegge T, von Lilienfeld OA, Walsh A. Accelerated chemical science with AI. DIGITAL DISCOVERY 2024; 3:23-33. [PMID: 38239898 PMCID: PMC10793638 DOI: 10.1039/d3dd00213f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/06/2023] [Indexed: 01/22/2024]
Abstract
In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.
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Affiliation(s)
- Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University Seoul Republic of Korea
| | - Alán Aspuru-Guzik
- Departments of Chemistry, Computer Science, University of Toronto St. George Campus Toronto ON Canada
- Acceleration Consortium and Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling (COSMO), École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Ganna Gryn'ova
- Heidelberg Institute for Theoretical Studies (HITS gGmbH) 69118 Heidelberg Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University 69120 Heidelberg Germany
| | - Bartosz Grzybowski
- Center for Algorithmic and Robotized Synthesis (CARS), Institute for Basic Science (IBS) Ulsan Republic of Korea
- Institute of Organic Chemistry, Polish Academy of Sciences Warsaw Poland
- Department of Chemistry, Ulsan National Institute of Science and Technology Ulsan Republic of Korea
| | - Geun Ho Gu
- Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH) Naju 58330 Republic of Korea
| | - Jason Hein
- Department of Chemistry, University of British Columbia Vancouver BC V6T 1Z1 Canada
| | - Kedar Hippalgaonkar
- School of Materials Science and Engineering, Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
- Institute of Materials Research and Engineering, Agency for Science Technology and Research 2 Fusionopolis Way, 08-03 Singapore 138634 Singapore
| | | | - Yousung Jung
- Department of Chemical and Biomolecular Engineering, KAIST Daejeon Republic of Korea
- School of Chemical and Biological Engineering, Interdisciplinary Program in Artificial Intelligence, Seoul National University 1 Gwanak-ro, Gwanak-gu Seoul 08826 Republic of Korea
| | - Seonah Kim
- Department of Chemistry, Colorado State University 1301 Center Avenue Fort Collins CO 80523 USA
| | - Woo Youn Kim
- Department of Chemistry, KAIST Daejeon Republic of Korea
| | - Seyed Mohamad Moosavi
- Chemical Engineering & Applied Chemistry, University of Toronto Toronto Ontario M5S 3E5 Canada
| | - Juhwan Noh
- Chemical Data-Driven Research Center, Korea Research Institute of Chemical Technology Daejeon 34114 Republic of Korea
| | | | - Joshua Schrier
- Department of Chemistry, Fordham University The Bronx NY 10458 USA
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence (LIAC) & National Centre of Competence in Research (NCCR) Catalysis, École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Koji Tsuda
- Graduate School of Frontier Sciences, The University of Tokyo Kashiwa Chiba 277-8561 Japan
- Center for Basic Research on Materials, National Institute for Materials Science Tsukuba Ibaraki 305-0044 Japan
- RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan
| | - Tejs Vegge
- Department of Energy Conversion and Storage, Technical University of Denmark 301 Anker Engelunds vej, Kongens Lyngby Copenhagen 2800 Denmark
| | - O Anatole von Lilienfeld
- Acceleration Consortium and Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada
- Departments of Chemistry, Materials Science and Engineering, and Physics, University of Toronto, St George Campus Toronto ON Canada
- Machine Learning Group, Technische Universität Berlin and Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany
| | - Aron Walsh
- Department of Materials, Imperial College London London SW7 2AZ UK
- Department of Physics, Ewha Women's University Seoul Republic of Korea
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Klucznik T, Syntrivanis LD, Baś S, Mikulak-Klucznik B, Moskal M, Szymkuć S, Mlynarski J, Gadina L, Beker W, Burke MD, Tiefenbacher K, Grzybowski BA. Computational prediction of complex cationic rearrangement outcomes. Nature 2024; 625:508-515. [PMID: 37967579 PMCID: PMC10864989 DOI: 10.1038/s41586-023-06854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 11/08/2023] [Indexed: 11/17/2023]
Abstract
Recent years have seen revived interest in computer-assisted organic synthesis1,2. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field1,3-7, including examples leading to advanced natural products6,7. Such methods typically operate on full, literature-derived 'substrate(s)-to-product' reaction rules and cannot be easily extended to the analysis of reaction mechanisms. Here we show that computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of some of the most complex organic transformations: namely, cationic rearrangements. Such rearrangements are a cornerstone of organic chemistry textbooks and entail notable changes in the molecule's carbon skeleton8-12. The algorithm we describe and deploy at https://HopCat.allchemy.net/ generates, within minutes, networks of possible mechanistic steps, traces plausible step sequences and calculates expected product distributions. We validate this algorithm by three sets of experiments whose analysis would probably prove challenging even to highly trained chemists: (1) predicting the outcomes of tail-to-head terpene (THT) cyclizations in which substantially different outcomes are encoded in modular precursors differing in minute structural details; (2) comparing the outcome of THT cyclizations in solution or in a supramolecular capsule; and (3) analysing complex reaction mixtures. Our results support a vision in which computers no longer just manipulate known reaction types1-7 but will help rationalize and discover new, mechanistically complex transformations.
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Affiliation(s)
- Tomasz Klucznik
- Allchemy, Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Leonidas-Dimitrios Syntrivanis
- Roger Adams Laboratory, School of Chemical Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Chemistry, University of Basel, Basel, Switzerland.
| | - Sebastian Baś
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
- Faculty of Chemistry, Jagiellonian University, Krakow, Poland
| | - Barbara Mikulak-Klucznik
- Allchemy, Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | | | | | - Jacek Mlynarski
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Louis Gadina
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Wiktor Beker
- Allchemy, Highland, IN, USA.
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland.
| | - Martin D Burke
- Roger Adams Laboratory, School of Chemical Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Molecule Maker Laboratory Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Molecule Maker Laboratory at the 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.
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Konrad Tiefenbacher
- Department of Chemistry, University of Basel, Basel, Switzerland.
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
| | - Bartosz A Grzybowski
- Allchemy, Highland, IN, USA.
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland.
- IBS Center for Algorithmic and Robotized Synthesis, CARS, Eonyang-eup, Ulju-gun, Ulsan, South Korea.
- Department of Chemistry, UNIST, Eonyang-eup, Ulju-gun, Ulsan, South Korea.
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5
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Grzybowski BA, Badowski T, Molga K, Szymkuć S. Network search algorithms and scoring functions for advanced‐level computerized synthesis planning. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Bartosz A. Grzybowski
- Institute of Organic Chemistry, Polish Academy of Sciences Warsaw Poland
- Center for Soft and Living Matter, Institute for Basic Science (IBS) Ulsan Republic of Korea
- Department of Chemistry Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea
| | - Tomasz Badowski
- Institute of Organic Chemistry, Polish Academy of Sciences Warsaw Poland
| | - Karol Molga
- Institute of Organic Chemistry, Polish Academy of Sciences Warsaw Poland
| | - Sara Szymkuć
- Institute of Organic Chemistry, Polish Academy of Sciences Warsaw Poland
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6
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Computer-designed repurposing of chemical wastes into drugs. Nature 2022; 604:668-676. [PMID: 35478240 DOI: 10.1038/s41586-022-04503-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 02/03/2022] [Indexed: 01/30/2023]
Abstract
As the chemical industry continues to produce considerable quantities of waste chemicals1,2, it is essential to devise 'circular chemistry'3-8 schemes to productively back-convert at least a portion of these unwanted materials into useful products. Despite substantial progress in the degradation of some classes of harmful chemicals9, work on 'closing the circle'-transforming waste substrates into valuable products-remains fragmented and focused on well known areas10-15. Comprehensive analyses of which valuable products are synthesizable from diverse chemical wastes are difficult because even small sets of waste substrates can, within few steps, generate millions of putative products, each synthesizable by multiple routes forming densely connected networks. Tracing all such syntheses and selecting those that also meet criteria of process and 'green' chemistries is, arguably, beyond the cognition of human chemists. Here we show how computers equipped with broad synthetic knowledge can help address this challenge. Using the forward-synthesis Allchemy platform16, we generate giant synthetic networks emanating from approximately 200 waste chemicals recycled on commercial scales, retrieve from these networks tens of thousands of routes leading to approximately 300 important drugs and agrochemicals, and algorithmically rank these syntheses according to the accepted metrics of sustainable chemistry17-19. Several of these routes we validate by experiment, including an industrially realistic demonstration on a 'pharmacy on demand' flow-chemistry platform20. Wide adoption of computerized waste-to-valuable algorithms can accelerate productive reuse of chemicals that would otherwise incur storage or disposal costs, or even pose environmental hazards.
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7
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Szymkuć S, Badowski T, Grzybowski BA. Is Organic Chemistry Really Growing Exponentially? Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202111540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Sara Szymkuć
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
- Allchemy, Inc. Highland IN USA
| | - Tomasz Badowski
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
- Allchemy, Inc. Highland IN USA
| | - Bartosz A. Grzybowski
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
- Allchemy, Inc. Highland IN USA
- IBS Center for Soft and Living Matter and Department of Chemistry UNIST 50, UNIST-gil, Eonyang-eup, Ulju-gun Ulsan South Korea
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8
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Szymkuć S, Badowski T, Grzybowski BA. Is Organic Chemistry Really Growing Exponentially? Angew Chem Int Ed Engl 2021; 60:26226-26232. [PMID: 34558168 DOI: 10.1002/anie.202111540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Indexed: 11/05/2022]
Abstract
In terms of molecules and specific reaction examples, organic chemistry features an impressive, exponential growth. However, new reaction classes/types that fuel this growth are being discovered at a much slower and only linear (or even sublinear) rate. The proportion of newly discovered reaction types to all reactions being performed keeps decreasing, suggesting that synthetic chemistry becomes more reliant on reusing the well-known methods. The newly discovered chemistries are more complex than decades ago and allow for the rapid construction of complex scaffolds in fewer numbers of steps. We study these and other trends in the function of time, reaction-type popularity and complexity based on the algorithm that extracts generalized reaction class templates. These analyses are useful in the context of computer-assisted synthesis, machine learning (to estimate the numbers of models with sufficient reaction statistics), and identifying erroneous entries in reaction databases.
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Affiliation(s)
- Sara Szymkuć
- Institute of Organic Chemistry, Polish Academy of Sciences, Ul. Kasprzaka 44/52, 01-224, Warsaw, Poland.,Allchemy, Inc., Highland, IN, USA
| | - Tomasz Badowski
- Institute of Organic Chemistry, Polish Academy of Sciences, Ul. Kasprzaka 44/52, 01-224, Warsaw, Poland.,Allchemy, Inc., Highland, IN, USA
| | - Bartosz A Grzybowski
- Institute of Organic Chemistry, Polish Academy of Sciences, Ul. Kasprzaka 44/52, 01-224, Warsaw, Poland.,Allchemy, Inc., Highland, IN, USA.,IBS Center for Soft and Living Matter and Department of Chemistry, UNIST, 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, South Korea
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9
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Wang Z, Zhang W, Liu B. Computational Analysis of Synthetic Planning: Past and Future. CHINESE J CHEM 2021. [DOI: 10.1002/cjoc.202100273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Zhuang Wang
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
| | - Wenhan Zhang
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
| | - Bo Liu
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
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10
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Sacha M, Błaż M, Byrski P, Dąbrowski-Tumański P, Chromiński M, Loska R, Włodarczyk-Pruszyński P, Jastrzębski S. Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits. J Chem Inf Model 2021; 61:3273-3284. [PMID: 34251814 DOI: 10.1021/acs.jcim.1c00537] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical reactions. With this in mind, we present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large data sets. We argue that representing the reaction as a sequence of edits enables MEGAN to efficiently explore the space of plausible chemical reactions, maintaining the flexibility of modeling the reaction in an end-to-end fashion and achieving state-of-the-art accuracy in standard benchmarks. Code and trained models are made available online at https://github.com/molecule-one/megan.
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Affiliation(s)
| | | | | | - Paweł Dąbrowski-Tumański
- Molecule One, Warsaw 00-815, Poland.,Faculty of Mathematics and Natural Sciences, School of Exact Sciences, Cardinal Stefan Wyszynski University, Warsaw 01-815, Poland
| | | | - Rafał Loska
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw 01-224, Poland
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11
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Molga K, Szymkuć S, Grzybowski BA. Chemist Ex Machina: Advanced Synthesis Planning by Computers. Acc Chem Res 2021; 54:1094-1106. [PMID: 33423460 DOI: 10.1021/acs.accounts.0c00714] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Teaching computers to plan multistep syntheses of arbitrary target molecules-including natural products-has been one of the oldest challenges in chemistry, dating back to the 1960s. This Account recapitulates two decades of our group's work on the software platform called Chematica, which very recently achieved this long-sought objective and has been shown capable of planning synthetic routes to complex natural products, several of which were validated in the laboratory.For the machine to plan syntheses at an expert level, it must know the rules describing chemical reactions and use these rules to expand and search the networks of synthetic options. The rules must be of high quality: They must delineate accurately the scope of admissible substituents, capture all relevant stereochemical information, detect potential reactivity conflicts, and protection requirements. They should yield only those synthons that are chemically stable and energetically allowed (e.g., not too strained) and should be able to extrapolate beyond examples already published in the literature. In parallel, the network-search algorithms must be able to assign meaningful scores to the sets of synthons they encounter, make judicious choices which of the network's branches to expand, and when to withdraw from unpromising ones. They must be able to strategize over multiple steps to resolve intermittent reactivity conflicts, exchange functional groups, or overcome local maxima of molecular complexity.Meeting all these requirements makes the problem of computer-driven retrosynthesis very multifaceted, combining expert and AI approaches further supplemented by quantum-mechanical and molecular-mechanics calculations. Development of Chematica has been a very long and gradual process because all these components are needed. Any shortcuts-for example, reliance on only expert or only data-based approaches-yield chemically naïve and often erroneous syntheses, especially for complex targets. On the bright side, once all the requisite algorithms are implemented-as they now are-they not only streamline conventional synthetic planning but also enable completely new modalities that would challenge any human chemist, for example, synthesis with multiple constraints imposed simultaneously or library-wide syntheses in which the machine constructs "global plans" leading to multiple targets and benefiting from the use of common intermediates. These types of analyses will have profound impact on the practice of chemical industry, designing more economical, more green, and less hazardous pathways.
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Affiliation(s)
- Karol Molga
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, 01-224, Warsaw, Poland
| | - Sara Szymkuć
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, 01-224, Warsaw, Poland
| | - Bartosz A. Grzybowski
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, 01-224, Warsaw, Poland
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan 44919, Republic of Korea
- Department of Chemistry, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, Republic of Korea
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12
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Finnigan W, Hepworth LJ, Flitsch SL, Turner NJ. RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades. Nat Catal 2021; 4:98-104. [PMID: 33604511 PMCID: PMC7116764 DOI: 10.1038/s41929-020-00556-z] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
As the enzyme toolbox for biocatalysis has expanded, so has the potential for the construction of powerful enzymatic cascades for efficient and selective synthesis of target molecules. Additionally, recent advances in computer-aided synthesis planning are revolutionising synthesis design in both synthetic biology and organic chemistry. However, the potential for biocatalysis is not well captured by tools currently available in either field. Here we present RetroBioCat, an intuitive and accessible tool for computer-aided design of biocatalytic cascades, freely available at retrobiocat.com. Our approach uses a set of expertly encoded reaction rules encompassing the enzyme toolbox for biocatalysis, and a system for identifying literature precedent for enzymes with the correct substrate specificity where this is available. Applying these rules for automated biocatalytic retrosynthesis, we show our tool to be capable of identifying promising biocatalytic pathways to target molecules, validated using a test-set of recent cascades described in the literature.
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Affiliation(s)
- William Finnigan
- Department of Chemistry, University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, M1 7DN, Manchester, UK
| | - Lorna J Hepworth
- Department of Chemistry, University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, M1 7DN, Manchester, UK
| | - Sabine L Flitsch
- Department of Chemistry, University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, M1 7DN, Manchester, UK
| | - Nicholas J Turner
- Department of Chemistry, University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, M1 7DN, Manchester, UK
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13
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Thakkar A, Johansson S, Jorner K, Buttar D, Reymond JL, Engkvist O. Artificial intelligence and automation in computer aided synthesis planning. REACT CHEM ENG 2021. [DOI: 10.1039/d0re00340a] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.
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Affiliation(s)
- Amol Thakkar
- Hit Discovery
- Discovery Sciences
- R&D
- AstraZeneca
- Gothenburg
| | | | - Kjell Jorner
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
| | - David Buttar
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry
- University of Bern
- 3012 Bern
- Switzerland
| | - Ola Engkvist
- Hit Discovery
- Discovery Sciences
- R&D
- AstraZeneca
- Gothenburg
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14
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Computational planning of the synthesis of complex natural products. Nature 2020; 588:83-88. [PMID: 33049755 DOI: 10.1038/s41586-020-2855-y] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 10/06/2020] [Indexed: 12/27/2022]
Abstract
Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years1-7. However, the field has progressed greatly since the development of early programs such as LHASA1,7, for which reaction choices at each step were made by human operators. Multiple software platforms6,8-14 are now capable of completely autonomous planning. But these programs 'think' only one step at a time and have so far been limited to relatively simple targets, the syntheses of which could arguably be designed by human chemists within minutes, without the help of a computer. Furthermore, no algorithm has yet been able to design plausible routes to complex natural products, for which much more far-sighted, multistep planning is necessary15,16 and closely related literature precedents cannot be relied on. Here we demonstrate that such computational synthesis planning is possible, provided that the program's knowledge of organic chemistry and data-based artificial intelligence routines are augmented with causal relationships17,18, allowing it to 'strategize' over multiple synthetic steps. Using a Turing-like test administered to synthesis experts, we show that the routes designed by such a program are largely indistinguishable from those designed by humans. We also successfully validated three computer-designed syntheses of natural products in the laboratory. Taken together, these results indicate that expert-level automated synthetic planning is feasible, pending continued improvements to the reaction knowledge base and further code optimization.
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15
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Wołos A, Roszak R, Żądło-Dobrowolska A, Beker W, Mikulak-Klucznik B, Spólnik G, Dygas M, Szymkuć S, Grzybowski BA. Synthetic connectivity, emergence, and
self-regeneration in the network of prebiotic
chemistry. Science 2020; 369:369/6511/eaaw1955. [DOI: 10.1126/science.aaw1955] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 03/28/2020] [Accepted: 07/24/2020] [Indexed: 12/13/2022]
Abstract
The challenge of prebiotic chemistry is to
trace the syntheses of life’s key building blocks
from a handful of primordial substrates. Here we
report a forward-synthesis algorithm that
generates a full network of prebiotic chemical
reactions accessible from these substrates under
generally accepted conditions. This network
contains both reported and previously unidentified
routes to biotic targets, as well as plausible
syntheses of abiotic molecules. It also exhibits
three forms of nontrivial chemical emergence, as
the molecules within the network can act as
catalysts of downstream reaction types; form
functional chemical systems, including
self-regenerating cycles; and produce surfactants
relevant to primitive forms of biological
compartmentalization. To support these claims,
computer-predicted, prebiotic syntheses of several
biotic molecules as well as a multistep,
self-regenerative cycle of iminodiacetic acid were
validated by experiment.
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Affiliation(s)
- Agnieszka Wołos
- Institute of Organic Chemistry,
Polish Academy of Sciences, Warsaw,
Poland
- Allchemy, Inc., Highland, IN,
USA
| | - Rafał Roszak
- Institute of Organic Chemistry,
Polish Academy of Sciences, Warsaw,
Poland
- Allchemy, Inc., Highland, IN,
USA
| | | | - Wiktor Beker
- Institute of Organic Chemistry,
Polish Academy of Sciences, Warsaw,
Poland
- Allchemy, Inc., Highland, IN,
USA
| | - Barbara Mikulak-Klucznik
- Institute of Organic Chemistry,
Polish Academy of Sciences, Warsaw,
Poland
- Allchemy, Inc., Highland, IN,
USA
| | - Grzegorz Spólnik
- Institute of Organic Chemistry,
Polish Academy of Sciences, Warsaw,
Poland
| | - Mirosław Dygas
- Institute of Organic Chemistry,
Polish Academy of Sciences, Warsaw,
Poland
| | - Sara Szymkuć
- Institute of Organic Chemistry,
Polish Academy of Sciences, Warsaw,
Poland
- Allchemy, Inc., Highland, IN,
USA
| | - Bartosz A. Grzybowski
- Institute of Organic Chemistry,
Polish Academy of Sciences, Warsaw,
Poland
- Allchemy, Inc., Highland, IN,
USA
- Center for Soft and Living Matter of
Korea’s Institute for Basic Science (IBS), Ulsan,
South Korea
- Department of Chemistry, Ulsan
National Institute of Science and Technology,
Ulsan, South Korea
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16
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Szymkuć S, Gajewska EP, Molga K, Wołos A, Roszak R, Beker W, Moskal M, Dittwald P, Grzybowski BA. Computer-generated "synthetic contingency" plans at times of logistics and supply problems: scenarios for hydroxychloroquine and remdesivir. Chem Sci 2020; 11:6736-6744. [PMID: 33033595 PMCID: PMC7500088 DOI: 10.1039/d0sc01799j] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 06/02/2020] [Indexed: 01/21/2023] Open
Abstract
A computer program for retrosynthetic planning helps develop multiple "synthetic contingency" plans for hydroxychloroquine and also routes leading to remdesivir, both promising but yet unproven medications against COVID-19. These plans are designed to navigate, as much as possible, around known and patented routes and to commence from inexpensive and diverse starting materials, so as to ensure supply in case of anticipated market shortages of commonly used substrates. Looking beyond the current COVID-19 pandemic, development of similar contingency syntheses is advocated for other already-approved medications, in case such medications become urgently needed in mass quantities to face other public-health emergencies.
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Affiliation(s)
- Sara Szymkuć
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
| | - Ewa P Gajewska
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
| | - Karol Molga
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
| | - Agnieszka Wołos
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
| | - Rafał Roszak
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
| | - Wiktor Beker
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
| | - Martyna Moskal
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
| | - Piotr Dittwald
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
| | - Bartosz A Grzybowski
- Institute of Organic Chemistry , Polish Academy of Sciences , ul. Kasprzaka 44/52 , Warsaw 02-224 , Poland .
- IBS Center for Soft and Living Matter , 50, UNIST-gil, Eonyang-eup, Ulju-gun , Ulsan , 689-798 , South Korea
- Department of Chemistry , UNIST , 50, UNIST-gil, Eonyang-eup, Ulju-gun , Ulsan , 689-798 , South Korea
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17
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Fortunato ME, Coley CW, Barnes BC, Jensen KF. Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning. J Chem Inf Model 2020; 60:3398-3407. [PMID: 32568548 DOI: 10.1021/acs.jcim.0c00403] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high-accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access data sets of organic reactions with explicitly calculated template applicability and pretraining a template-relevance neural network on this augmented applicability data set, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small data set of well-curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating that these strategies can be very useful for small data sets.
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Affiliation(s)
- Michael E Fortunato
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Brian C Barnes
- Detonation Science and Modeling Branch, CCDC Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
| | - Klavs F Jensen
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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18
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Li X, Zhang S, Xu L, Hong X. Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.202000959] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Xin Li
- Department of Chemistry Zhejiang University 38 Zheda Road Hangzhou 310027 China
| | - Shuo‐Qing Zhang
- Department of Chemistry Zhejiang University 38 Zheda Road Hangzhou 310027 China
| | - Li‐Cheng Xu
- Department of Chemistry Zhejiang University 38 Zheda Road Hangzhou 310027 China
| | - Xin Hong
- Department of Chemistry Zhejiang University 38 Zheda Road Hangzhou 310027 China
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19
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Li X, Zhang S, Xu L, Hong X. Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning. Angew Chem Int Ed Engl 2020; 59:13253-13259. [DOI: 10.1002/anie.202000959] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 03/30/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Xin Li
- Department of Chemistry Zhejiang University 38 Zheda Road Hangzhou 310027 China
| | - Shuo‐Qing Zhang
- Department of Chemistry Zhejiang University 38 Zheda Road Hangzhou 310027 China
| | - Li‐Cheng Xu
- Department of Chemistry Zhejiang University 38 Zheda Road Hangzhou 310027 China
| | - Xin Hong
- Department of Chemistry Zhejiang University 38 Zheda Road Hangzhou 310027 China
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20
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21
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Fitzpatrick DE, O'Brien M, Ley SV. A tutored discourse on microcontrollers, single board computers and their applications to monitor and control chemical reactions. REACT CHEM ENG 2020. [DOI: 10.1039/c9re00407f] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This Tutored Discourse constitutes a preliminary exposure on how synthesis chemists can engage positively with inexpensive, low-power microcontrollers to aid control, monitoring and optimisation of chemical reactions.
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Affiliation(s)
| | - Matthew O'Brien
- Department of Chemistry
- Keele University
- Staffordshire ST5 5BG
- UK
| | - Steven V. Ley
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
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22
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Struble TJ, Coley CW, Jensen KF. Multitask prediction of site selectivity in aromatic C–H functionalization reactions. REACT CHEM ENG 2020. [DOI: 10.1039/d0re00071j] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Aromatic C–H functionalization reactions are an important part of the synthetic chemistry toolbox.
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Affiliation(s)
- Thomas J. Struble
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- USA
| | - Connor W. Coley
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- USA
| | - Klavs F. Jensen
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- USA
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23
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Badowski T, Gajewska EP, Molga K, Grzybowski BA. Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201912083] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Tomasz Badowski
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
| | - Ewa P. Gajewska
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
| | - Karol Molga
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
| | - Bartosz A. Grzybowski
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
- IBS Center for Soft and Living Matter and Department of Chemistry UNIST 50, UNIST-gil, Eonyang-eup, Ulju-gun Ulsan South Korea
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24
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Badowski T, Gajewska EP, Molga K, Grzybowski BA. Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning. Angew Chem Int Ed Engl 2019; 59:725-730. [DOI: 10.1002/anie.201912083] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Indexed: 12/27/2022]
Affiliation(s)
- Tomasz Badowski
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
| | - Ewa P. Gajewska
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
| | - Karol Molga
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
| | - Bartosz A. Grzybowski
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
- IBS Center for Soft and Living Matter and Department of Chemistry UNIST 50, UNIST-gil, Eonyang-eup, Ulju-gun Ulsan South Korea
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