1
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Noto N, Yada A, Yanai T, Saito S. Machine-Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)-Salt-Induced Synthesis of Phenols. Angew Chem Int Ed Engl 2023; 62:e202219107. [PMID: 36645619 DOI: 10.1002/anie.202219107] [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: 12/25/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/17/2023]
Abstract
Catalytic systems using a small amount of organic photosensitizer for the activation of an inorganic (on-demand ligand-free) nickel(II) salt represent a cost-effective method for cross-coupling reactions, while C(sp2 )-O bond formation remains less developed. Herein, we report a strategy for the synthesis of phenols with a nickel(II) salt and an organic photosensitizer, which was identified via an investigation into the catalytic activity of 60 organic photosensitizers consisting of various electron donor and acceptor moieties. To examine the effect of multiple intractable parameters on the catalytic activity of photosensitizers, machine-learning (ML) models were developed, wherein we embedded descriptors representing their physical and structural properties, which were obtained from DFT calculations and RDKit, respectively. The study clarified that integrating both DFT- and RDKit-derived descriptors in ML models balances higher "precision" and "recall" across a wide range of search space relative to using only one of the two descriptor sets.
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Affiliation(s)
- Naoki Noto
- Integrated Research Consortium on Chemical Sciences (IRCCS), Nagoya University, Nagoya, Aichi, 464-8602, Japan
| | - Akira Yada
- Interdisciplinary Research Center for Catalytic Chemistry, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki, 305-8565, Japan
| | - Takeshi Yanai
- Institute of Transformative Bio-Molecules (WPI-ITbM) and Graduate School of Science, Nagoya University, Nagoya, Aichi, 464-8602, Japan
| | - Susumu Saito
- Integrated Research Consortium on Chemical Sciences (IRCCS) and Graduate School of Science, Nagoya University, Nagoya, Aichi, 464-8602, Japan
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2
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Strieth-Kalthoff F, Sandfort F, Kühnemund M, Schäfer FR, Kuchen H, Glorius F. Machine Learning for Chemical Reactivity: The Importance of Failed Experiments. Angew Chem Int Ed Engl 2022; 61:e202204647. [PMID: 35512117 DOI: 10.1002/anie.202204647] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Indexed: 12/27/2022]
Abstract
Assessing the outcomes of chemical reactions in a quantitative fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities of high-quality data, the availability of which is limited: Main reasons for this include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In a series of case studies, we investigate the impact of these biases for drawing general conclusions from chemical reaction data, revealing the utmost importance of "negative" examples. Eventually, case studies into data expansion approaches showcase directions to circumvent these limitations-and demonstrate perspectives towards a long-term data quality enhancement in chemistry.
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Affiliation(s)
- Felix Strieth-Kalthoff
- Westfälische Wilhelms-Universität Münster, Organisch-Chemisches Institut, Corrensstr. 40, 48149, Münster, Germany
| | - Frederik Sandfort
- Westfälische Wilhelms-Universität Münster, Organisch-Chemisches Institut, Corrensstr. 40, 48149, Münster, Germany
| | - Marius Kühnemund
- Westfälische Wilhelms-Universität Münster, Department for Information Systems, Leonardo-Campus 3, 48149, Münster, Germany
| | - Felix R Schäfer
- Westfälische Wilhelms-Universität Münster, Organisch-Chemisches Institut, Corrensstr. 40, 48149, Münster, Germany
| | - Herbert Kuchen
- Westfälische Wilhelms-Universität Münster, Department for Information Systems, Leonardo-Campus 3, 48149, Münster, Germany
| | - Frank Glorius
- Westfälische Wilhelms-Universität Münster, Organisch-Chemisches Institut, Corrensstr. 40, 48149, Münster, Germany
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3
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Strieth‐Kalthoff F, Sandfort F, Kühnemund M, Schäfer FR, Kuchen H, Glorius F. Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202204647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Felix Strieth‐Kalthoff
- Westfälische Wilhelms-Universität Münster Organisch-Chemisches Institut Corrensstr. 40 48149 Münster Deutschland
| | - Frederik Sandfort
- Westfälische Wilhelms-Universität Münster Organisch-Chemisches Institut Corrensstr. 40 48149 Münster Deutschland
| | - Marius Kühnemund
- Westfälische Wilhelms-Universität Münster Department for Information Systems Leonardo-Campus 3 48149 Münster Deutschland
| | - Felix R. Schäfer
- Westfälische Wilhelms-Universität Münster Organisch-Chemisches Institut Corrensstr. 40 48149 Münster Deutschland
| | - Herbert Kuchen
- Westfälische Wilhelms-Universität Münster Department for Information Systems Leonardo-Campus 3 48149 Münster Deutschland
| | - Frank Glorius
- Westfälische Wilhelms-Universität Münster Organisch-Chemisches Institut Corrensstr. 40 48149 Münster Deutschland
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4
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Chauhan ANS, Mali G, Erande RD. Regioselectivity Switch Towards the Development of Innovative Diels‐Alder Cycloaddition and Productive Applications in Organic Synthesis. ASIAN J ORG CHEM 2022. [DOI: 10.1002/ajoc.202100793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Amar Nath Singh Chauhan
- IIT Jodhpur: Indian Institute of Technology Jodhpur Chemistry Chemistry departmentIIT Jodhpur 342037 Jodhpur INDIA
| | - Ghanshyam Mali
- IIT Jodhpur: Indian Institute of Technology Jodhpur chemistry Chemistry departmentIIT Jodhpur 342037 Jodhpur INDIA
| | - Rohan D. Erande
- Indian Institute of Technology Jodhpur Chemistry Office 103, Department of Chemistry, IIT Jodhpur, N.H. 62, Nagaur Road, Karwar 342037 Jodhpur INDIA
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5
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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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6
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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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7
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Raman G. Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning. ChemistrySelect 2021. [DOI: 10.1002/slct.202102890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Ganesan Raman
- Reliance Research & Development Center Reliance Corporate Park, Reliance Industries Limited Thane-Belapur Road, Ghansoli Navi Mumbai India 400701
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8
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Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202106880] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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9
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Xu LC, Zhang SQ, Li X, Tang MJ, Xie PP, Hong X. Towards Data-driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning. Angew Chem Int Ed Engl 2021; 60:22804-22811. [PMID: 34370892 DOI: 10.1002/anie.202106880] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/14/2021] [Indexed: 11/09/2022]
Abstract
Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time- and resource-consuming process due to the lack of predictive catalyst design strategy. Targeting the data-driven design of asymmetric catalysis, we herein report the development of a standardized database that contains the detailed information of over 12000 literature asymmetric hydrogenations of olefins. This database provides a valuable platform for the machine learning applications in asymmetric catalysis. Based on this database, we developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem and will facilitate the reaction optimization with new olefin substrate in catalysis screening.
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Affiliation(s)
- Li-Cheng Xu
- Zhejiang University, Department of Chemistry, CHINA
| | | | - Xin Li
- Zhejiang University, Department of Chemistry, CHINA
| | | | - Pei-Pei Xie
- Zhejiang University, Department of Chemistry, CHINA
| | - Xin Hong
- Zhejiang University, Department of Chemistry, 38 Zheda Road, 310028, Hangzhou, CHINA
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10
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Moskal M, Beker W, Szymkuć S, Grzybowski BA. Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202101986] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Martyna Moskal
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
- Allchemy, Inc. Highland IN USA
| | - Wiktor Beker
- Institute of Organic Chemistry Polish Academy of Sciences Ul. Kasprzaka 44/52 01-224 Warsaw Poland
- Allchemy, Inc. Highland IN USA
| | - Sara Szymkuć
- 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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11
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Moskal M, Beker W, Szymkuć S, Grzybowski BA. Scaffold-Directed Face Selectivity Machine-Learned from Vectors of Non-covalent Interactions. Angew Chem Int Ed Engl 2021; 60:15230-15235. [PMID: 33876554 DOI: 10.1002/anie.202101986] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/29/2021] [Indexed: 11/06/2022]
Abstract
This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms.
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Affiliation(s)
- Martyna Moskal
- Institute of Organic Chemistry, Polish Academy of Sciences, Ul. Kasprzaka 44/52, 01-224, Warsaw, Poland.,Allchemy, Inc., Highland, IN, USA
| | - Wiktor Beker
- Institute of Organic Chemistry, Polish Academy of Sciences, Ul. Kasprzaka 44/52, 01-224, Warsaw, Poland.,Allchemy, Inc., Highland, IN, USA
| | - Sara Szymkuć
- 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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12
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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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13
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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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