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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Zhang SQ, Xu LC, Li SW, Oliveira JCA, Li X, Ackermann L, Hong X. Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis. Chemistry 2023; 29:e202202834. [PMID: 36206170 PMCID: PMC10099903 DOI: 10.1002/chem.202202834] [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: 09/12/2022] [Indexed: 11/29/2022]
Abstract
Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.
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Affiliation(s)
- Shuo-Qing Zhang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China
| | - Li-Cheng Xu
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China
| | - Shu-Wen Li
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China
| | - João C A Oliveira
- Institut für Organische und Biomolekulare Chemie, Wöhler Research Institute for Sustainable Chemistry (WISCh), Georg-August-Universität, Tammannstraße 2, 37077, Göttingen, Germany
| | - Xin Li
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China
| | - Lutz Ackermann
- Institut für Organische und Biomolekulare Chemie, Wöhler Research Institute for Sustainable Chemistry (WISCh), Georg-August-Universität, Tammannstraße 2, 37077, Göttingen, Germany
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China.,Beijing National Laboratory for Molecular Sciences, Zhongguancun North First Street No. 2, Beijing, 100190, P. R. China.,Key Laboratory of Precise Synthesis of, Functional Molecules of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang Province, P. R. China
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3
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Le Pogam P, Papon N, Beniddir MA, Courdavault V. Computer-Assisted Design of Sustainable Syntheses of Pharmaceuticals and Agrochemicals from Industrial Wastes. CHEMSUSCHEM 2022; 15:e202201125. [PMID: 35894947 DOI: 10.1002/cssc.202201125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/24/2022] [Indexed: 06/15/2023]
Abstract
Computer-based strategies vastly enhanced the field of analytical chemistry. The impact of data-driven technologies in shaping organic chemistry strategies long remained comparatively elusive but various tools recently emerged to computationally plan multistep organic syntheses. A recent study elegantly takes benefit of an in-house library of chemical reactions enriched with various metadata to provide numerous, reliable and realistic organic chemistry workflows to structurally-varied drugs of interest, from locally available industrial by-products. The retrieval of the different synthetic pathways and a scoring based on different features, especially comprising sustainability considerations, are also proposed.
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Affiliation(s)
- Pierre Le Pogam
- Équipe Chimie des Substances Naturelles, BioCIS, Université Paris-Saclay, CNRS, 92290, Châtenay-Malabry, France
| | - Nicolas Papon
- Univ Angers, Univ Brest, IRF, SFR ICAT, F-49000, Angers, France
| | - Mehdi A Beniddir
- Équipe Chimie des Substances Naturelles, BioCIS, Université Paris-Saclay, CNRS, 92290, Châtenay-Malabry, France
| | - Vincent Courdavault
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Université de Tours, 37200, Tours, France
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4
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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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5
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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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6
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Kitamura Y, Terado E, Zhang Z, Yoshikawa H, Inose T, Uji-I H, Tanimizu M, Inokuchi A, Kamakura Y, Tanaka D. Failure-Experiment-Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks with Two-Dimensional Secondary Building Units*. Chemistry 2021; 27:16347-16353. [PMID: 34623003 DOI: 10.1002/chem.202102404] [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: 06/03/2021] [Indexed: 11/12/2022]
Abstract
Novel metal-organic frameworks containing lanthanide double-layer-based secondary building units (KGF-3) were synthesized by using machine learning (ML). Isolating pure KGF-3 was challenging, and the synthesis was not reproducible because impurity phases were frequently obtained under the same synthetic conditions. Thus, dominant factors for the synthesis of KGF-3 were identified, and its synthetic conditions were optimized by using two ML techniques. Cluster analysis was used to classify the obtained powder X-ray diffractometry patterns of the products and thus automatically determine whether the experiments were successful. Decision-tree analysis was used to visualize the experimental results, after extracting factors that mainly affected the synthetic reproducibility. Water-adsorption isotherms revealed that KGF-3 possesses unique hydrophilic pores. Impedance measurements demonstrated good proton conductivities (σ=5.2×10-4 S cm-1 for KGF-3(Y)) at a high temperature (363 K) and relative humidity of 95 % RH.
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Affiliation(s)
- Yu Kitamura
- Department of Chemistry, School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
| | - Emi Terado
- Department of Chemistry, School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
| | - Zechen Zhang
- Department of Nanotechnology for Sustainable Energy School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
| | - Hirofumi Yoshikawa
- Department of Nanotechnology for Sustainable Energy School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
| | - Tomoko Inose
- Research Institute for Electronic Science (RIES), Hokkaido University North 20 West 10, Kita Ward Sapporo, Hokkaido, 001-0020, Japan.,Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University, Yoshida, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Hiroshi Uji-I
- Research Institute for Electronic Science (RIES), Hokkaido University North 20 West 10, Kita Ward Sapporo, Hokkaido, 001-0020, Japan.,Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University, Yoshida, Sakyo-ku, Kyoto, 606-8501, Japan.,Department of Chemistry, Katholieke Universiteit Leuven, Celestijnenlaan 200F, Heverlee, 3001, Belgium
| | - Masaharu Tanimizu
- Department of Applied Chemistry for Environment School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
| | - Akihiro Inokuchi
- Department of Informatics School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
| | - Yoshinobu Kamakura
- Department of Chemistry, School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
| | - Daisuke Tanaka
- Department of Chemistry, School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan.,JST PRESTO, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
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7
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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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8
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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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9
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Puleo TR, Sujansky SJ, Wright SE, Bandar JS. Organic Superbases in Recent Synthetic Methodology Research. Chemistry 2021; 27:4216-4229. [DOI: 10.1002/chem.202003580] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Indexed: 12/23/2022]
Affiliation(s)
- Thomas R. Puleo
- Department of Chemistry Colorado State University Fort Collins Colorado 80523 USA
| | - Stephen J. Sujansky
- Department of Chemistry Colorado State University Fort Collins Colorado 80523 USA
| | - Shawn E. Wright
- Department of Chemistry Colorado State University Fort Collins Colorado 80523 USA
| | - Jeffrey S. Bandar
- Department of Chemistry Colorado State University Fort Collins Colorado 80523 USA
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