1
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Heiner BR, Handy KM, Devlin AM, Soucek JL, Pittsford AM, Turner DA, Petersen JP, Oliver AG, Corcelli SA, Kandel SA. Enantiopure molecules form apparently racemic monolayers of chiral cyclic pentamers. Phys Chem Chem Phys 2024. [PMID: 39319688 DOI: 10.1039/d4cp02094d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
Ultra-high vacuum scanning tunneling microscopy (UHV-STM) was used to investigate two related molecules pulse-deposited onto Au(111) surfaces: indoline-2-carboxylic acid and proline (pyrrolidine-2-carboxylic acid). Indoline-2-carboxylic acid and proline form both dimers and C5-symmetric "pinwheel" pentamers. Enantiomerically pure S-(-)-indoline-2-carboxylic acid and S-proline were used, and the pentamer structures observed for both were chiral. However, the presence of apparently equal numbers of 'right-' and 'left-handed' pinwheels is contrary to the general understanding that the chirality of the molecule dictates supramolecular chirality. A variety of computational methods were used to elucidate pentamer geometry for S-proline. Straightforward geometry optimization proved difficult, as the size of the cluster and the number of possible intermolecular interactions produced an interaction potential with multiple local minima. Instead, the Amber force field was used to exhaustively search all of phase space for chemically reasonable pentamer structures, producing a limited number of candidate structures that were then optimized as gas-phase clusters using density functional theory (DFT). The binding energies of the two lowest-energy pentamers on the Au(111) surface were then calculated by plane-wave DFT using the VASP software, and STM images predicted. These calculations indicate that the right- and left-handed pentamers are instead two different polymorphs.
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Affiliation(s)
- Benjamin R Heiner
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Kaitlyn M Handy
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Angela M Devlin
- Department of Chemistry and Biochemistry, Creighton University, Omaha, NE 68179, USA
| | - Jewel L Soucek
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Alexander M Pittsford
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | | | | | - Allen G Oliver
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Steven A Corcelli
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - S Alex Kandel
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
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2
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Deng YH, Sun TY, Wu YD. Understanding the Nonlinear Hammett Relationship in Osmylation of Olefins with OsO 4-Amine Ligands: Importance of Singlet-Diradical Character. J Org Chem 2024; 89:11173-11182. [PMID: 39072554 DOI: 10.1021/acs.joc.4c00693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Although the concerted [3 + 2] mechanism of osmium-catalyzed asymmetric dihydroxylation has been generally accepted, the unusual nonlinear Hammett relationship induced by amine-type ligands remains unexplained. To understand this, we carried out a density functional theory (DFT) study for the osmylation of substituted styrenes by the following: OsO4, OsO4-pyridine, OsO4-4-cyanopyridine, OsO4-4-pyrrolidinopyridine, and OsO4-quinuclidine. Calculations using the M06 functional successfully reproduce the experimentally observed nonlinear relationships. The transition states exhibit considerable singlet-diradical character, which causes the nonlinear Hammett relationship. Regardless of the presence or absence of an amine-type ligand, an electron donation from styrene to OsO4 is observed, indicating no mechanistic change. Calculations indicate that the electronic interaction between the amine-type ligand and styrene also influences the reaction rate.
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Affiliation(s)
- Yi-Hui Deng
- Key Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Shenzhen Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, PR China
| | - Tian-Yu Sun
- Key Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Shenzhen Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, PR China
- Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yun-Dong Wu
- Key Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Shenzhen Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, PR China
- Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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3
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Wiest O, Bauer C, Helquist P, Norrby PO, Genheden S. Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions. J Chem Inf Model 2024; 64:5796-5805. [PMID: 38995078 DOI: 10.1021/acs.jcim.4c00370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A novel approach to automated extraction of templates used in CASP that includes stereochemical information included in the US Patent and Trademark Office (USPTO) and an internal AstraZeneca database containing reactions from Reaxys, Pistachio, and AstraZeneca electronic lab notebooks is implemented in the freely available AiZynthFinder software. Three hundred sixty-seven templates covering reagent- and substrate-controlled as well as stereospecific reactions were extracted from the USPTO, while 20,724 templates were from the AstraZeneca database. The performance of these templates in multistep CASP is evaluated for 936 targets from the ChEMBL database and an in-house selection of 791 AZ designs. The potential and limitations are discussed for four case studies from ChEMBL and examples of FDA-approved drugs.
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Affiliation(s)
- Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Christoph Bauer
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden
| | - Paul Helquist
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Per-Ola Norrby
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden
| | - Samuel Genheden
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden
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4
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Laplaza R, Wodrich MD, Corminboeuf C. Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States. J Phys Chem Lett 2024; 15:7363-7370. [PMID: 38990895 DOI: 10.1021/acs.jpclett.4c01657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
The prediction of reaction selectivity is a challenging task for computational chemistry, not only because many molecules adopt multiple conformations but also due to the exponential relationship between effective activation energies and rate constants. To account for molecular flexibility, an increasing number of methods exist that generate conformational ensembles of transition state (TS) structures. Typically, these TS ensembles are Boltzmann weighted and used to compute selectivity assuming Curtin-Hammett conditions. This strategy, however, can lead to erroneous predictions if the appropriate filtering of the conformer ensembles is not conducted. Here, we demonstrate how any possible selectivity can be obtained by processing the same sets of TS ensembles for a model reaction. To address the burdensome filtering task in a consistent and automated way, we introduce marc, a tool for the modular analysis of representative conformers that aids in avoiding human errors while minimizing the number of reoptimization computations needed to obtain correct reaction selectivity.
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Affiliation(s)
- Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Matthew D Wodrich
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Clemence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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5
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Chen F, Chen Y, Chang XY, He D, Yang Q, Wang DZ, Xu C, Yu P, Xing X. Polarizability matters in enantio-selection. Nat Commun 2024; 15:3394. [PMID: 38649371 PMCID: PMC11035643 DOI: 10.1038/s41467-024-47813-4] [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: 06/05/2023] [Accepted: 03/22/2024] [Indexed: 04/25/2024] Open
Abstract
The prevalence of chirality, or, handedness in biological world is a fundamental phenomenon and a characteristic hallmark of life. Thus, understanding the origin of enantio-selection, i.e., the sense and magnitude of asymmetric induction, has been a long-pursued goal in asymmetric catalysis. Herein, we demonstrated a polarizability-derived electronic effect that was shown to be capable of rationalizing a broad range of stereochemical observations made in the field of asymmetric catalysis. This effect provided a consistent enantio-control model for the prediction of major enantiomers formed in a ruthenium-catalyzed asymmetric transfer hydrogenations of ketones. Direct and quantitative linear free energy relationships between substrates' local polarizabilities and observed enantio-selectivity were also revealed in three widely known asymmetric catalytic systems covering both reductions and oxidations. This broadly applicable polarizability-based electronic effect, in conjunction with conventional wisdom mainly leveraging on steric effect considerations, should aid rational design of enantio-selective processes for better production of chiral substances.
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Affiliation(s)
- Fumin Chen
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China
- Shenzhen Grubbs Institute and Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yu Chen
- Shenzhen Grubbs Institute and Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Xiao-Yong Chang
- Shenzhen Grubbs Institute and Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Dongxu He
- Shenzhen Grubbs Institute and Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Qingjing Yang
- Shenzhen Grubbs Institute and Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | | | - Chen Xu
- Shenzhen Grubbs Institute and Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Peiyuan Yu
- Shenzhen Grubbs Institute and Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Xiangyou Xing
- Shenzhen Grubbs Institute and Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China.
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6
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Kammeraad JA, Das S, Argüelles AJ, Sayyed FB, Zimmerman PM. Conformational Sampling over Transition-Metal-Catalyzed Reaction Pathways: Toward Revealing Atroposelectivity. Org Lett 2024; 26:2867-2871. [PMID: 38241482 PMCID: PMC11135461 DOI: 10.1021/acs.orglett.3c04047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
The Py-Conformational-Sampling (PyCoSa) technique is introduced as a systematic computational means to sample the configurational space of transition-metal-catalyzed stereoselective reactions. When applied to atroposelective Suzuki-Miyaura coupling to create axially chiral biaryl products, the results show a range of mechanistic possibilities that include multiple low-energy channels through which C-C bonds can be formed.
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Affiliation(s)
- Joshua A Kammeraad
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1055, United States
| | - Soumik Das
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1055, United States
| | - Alonso J Argüelles
- Synthetic Molecule Design and Development, Eli Lilly, Eli Lilly, Indianapolis, Indiana 46221, United States
| | - Fareed Bhasha Sayyed
- Synthetic Molecule Design and Development, Eli Lilly Services India Pvt Ltd, Devarabeesanahalli, Bengaluru, Karnataka 560103, India
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1055, United States
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7
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Harabuchi Y, Yokoyama T, Matsuoka W, Oki T, Iwata S, Maeda S. Differentiating the Yield of Chemical Reactions Using Parameters in First-Order Kinetic Equations to Identify Elementary Steps That Control the Reactivity from Complicated Reaction Path Networks. J Phys Chem A 2024; 128:2883-2890. [PMID: 38564273 DOI: 10.1021/acs.jpca.4c00204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The yield of a chemical reaction is obtained by solving its rate equation. This study introduces an approach for differentiating yields by utilizing the parameters of the rate equation, which is expressed as a first-order linear differential equation. The yield derivative for a specific pair of reactants and products is derived by mathematically expressing the rate constant matrix contraction method, which is a simple kinetic analysis method. The parameters of the rate equation are the Gibbs energies of the intermediates and transition states in the reaction path network used to formulate the rate equation. Thus, our approach for differentiating the yield allows a numerical evaluation of the contribution of energy variation to the yield for each intermediate and transition state in the reaction path network. In other words, a comparison of these values automatically extracts the factors affecting the yield from a complicated reaction path network consisting of numerous reaction paths and intermediates. This study verifies the behavior of the proposed approach through numerical experiments on the reaction path networks of a model system and the Rh-catalyzed hydroformylation reaction. Moreover, the possibility of using this approach for designing ligands in organometallic catalysts is discussed.
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Affiliation(s)
- Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Tomohiko Yokoyama
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Wataru Matsuoka
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Taihei Oki
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Satoru Iwata
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
- JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
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8
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Okada H, Maeda S. On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set. ACS OMEGA 2024; 9:7123-7131. [PMID: 38371820 PMCID: PMC10870292 DOI: 10.1021/acsomega.3c09066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 02/20/2024]
Abstract
Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.
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Affiliation(s)
- Hiroaki Okada
- Graduate
School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
| | - Satoshi Maeda
- Department
of Chemistry, Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
- ERATO
Maeda Artificial Intelligence for Chemical Reaction Design and Discovery
Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Research
and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
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9
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Wang G, Wu X, Xin B, Gu X, Wang G, Zhang Y, Zhao J, Cheng X, Chen C, Ma J. Machine Learning in Unmanned Systems for Chemical Synthesis. Molecules 2023; 28:2232. [PMID: 36903478 PMCID: PMC10004533 DOI: 10.3390/molecules28052232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/05/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023] Open
Abstract
Chemical synthesis is state-of-the-art, and, therefore, it is generally based on chemical intuition or experience of researchers. The upgraded paradigm that incorporates automation technology and machine learning (ML) algorithms has recently been merged into almost every subdiscipline of chemical science, from material discovery to catalyst/reaction design to synthetic route planning, which often takes the form of unmanned systems. The ML algorithms and their application scenarios in unmanned systems for chemical synthesis were presented. The prospects for strengthening the connection between reaction pathway exploration and the existing automatic reaction platform and solutions for improving autonomation through information extraction, robots, computer vision, and intelligent scheduling were proposed.
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Affiliation(s)
- Guoqiang Wang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Xuefei Wu
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Bo Xin
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Xu Gu
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Gaobo Wang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Yong Zhang
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Jiabao Zhao
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Xu Cheng
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
- Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Chunlin Chen
- Department of Control Science and Intelligent Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, China
| | - Jing Ma
- Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
- Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
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10
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Wahlers J, Rosales AR, Berkel N, Forbes A, Helquist P, Norrby PO, Wiest O. A Quantum-Guided Molecular Mechanics Force Field for the Ferrocene Scaffold. J Org Chem 2022; 87:12334-12341. [PMID: 36066498 DOI: 10.1021/acs.joc.2c01553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Ferrocene derivatives have a wide range of applications, including as ligands in asymmetric catalysis, due to their chemical stability, rigid backbone, steric bulk, and ability to encode stereochemical information via planar chirality. Unfortunately, few of the available molecular mechanics force fields incorporate parameters for the accurate study of this important building block. Here, we present a MM3* force field for ferrocenyl ligands, which was generated using the quantum-guided molecular mechanics (Q2MM) method. Detailed validation by comparison to DFT calculations and crystal structures demonstrates the accuracy of the parameters and uncovers the physical origin of deviations through excess energy analysis. Combining the ferrocene force field with a force field for Pd-allyl complexes and comparing the crystal structures shows the compatibility with previously developed MM3* force fields. Finally, the ferrocene force field was combined with a previously published transition-state force field to predict the stereochemical outcomes of the aminations of Pd-allyl complexes with different amines and different chiral ferrocenyl ligands, with an R2 of ∼0.91 over 10 examples.
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Affiliation(s)
- Jessica Wahlers
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Anthony R Rosales
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Neil Berkel
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Aaron Forbes
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Paul Helquist
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Per-Ola Norrby
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, Pepparedsleden 1, Mölndal SE-431 83, Sweden
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
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11
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Menche M, Klein P, Hermsen M, Konrath R, Ghosh T, Wysocki J, Ernst M, Hashmi ASK, Schäfer A, Comba P, Schaub T. Ligand backbone influence on the enantioselectivity in the ruthenium‐catalyzed direct asymmetric reductive amination of ketones with NH3/H2 using binaphthyl‐substituted phosphines. ChemCatChem 2022. [DOI: 10.1002/cctc.202200543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Maximilian Menche
- BASF SE Computational Chemistry Carl-Bosch-Str. 38 67056 Ludwigshafen GERMANY
| | - Philippe Klein
- Heidelberg University Catalysis Research Laboratory (CaRLa) Im Neuenheimer Feld 584 69120 Heidelberg GERMANY
| | - Marko Hermsen
- Heidelberg University CaRLa Im Neuenheimer Feld 584 69120 Heidelberg GERMANY
| | - Robert Konrath
- BASF SE Organic Synthesis Carl-Bosch-Str. 38 67056 Ludwigshafen GERMANY
| | - Tamal Ghosh
- Heidelberg University CaRLa Im Neuenheimer Feld 584 69120 Heidelberg GERMANY
| | - Jedrzej Wysocki
- Heidelberg University CaRLa Im Neuenheimer Flel 584 69120 Heidelberg GERMANY
| | - Martin Ernst
- BASF SE Organic Synthesis Carl-Bosch-Str. 38 67056 Ludwigshafen GERMANY
| | - A. Stephen K. Hashmi
- Heidelberg University Organic Chemistry Im Neuenheimer Feld 270 69120 Heidelberg GERMANY
| | - Ansgar Schäfer
- BASF SE Computational Chemistry Carl-Bosch-Str. 38 67056 Ludwigshafen GERMANY
| | - Peter Comba
- Heidelberg University Inorganic Chemistry Im Neuenheimer Feld 270 69120 Heidelberg GERMANY
| | - Thomas Schaub
- BASF SE Synthesis and Homogeneous Catalysis Carl-Bosch-Strasse 38 67056 Ludwigshafen GERMANY
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12
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Lewis‐Atwell T, Townsend PA, Grayson MN. Machine learning activation energies of chemical reactions. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1593] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Toby Lewis‐Atwell
- Department of Computer Science, Faculty of Science University of Bath Bath UK
| | - Piers A. Townsend
- Department of Chemistry, Faculty of Science University of Bath Bath UK
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13
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Farrar EHE, Grayson MN. Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction. Chem Sci 2022; 13:7594-7603. [PMID: 35872815 PMCID: PMC9242013 DOI: 10.1039/d2sc02925a] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/08/2022] [Indexed: 11/21/2022] Open
Abstract
Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C-C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol-1 and substantially better than SQM methods without ML correction (5.71 kcal mol-1). Predictive power is shown to hold when the ML models are applied to an unseen set of compounds from the toxicology literature. Mechanistic insight is also achieved via the generation of full SQM transition state (TS) structures which are found to be very good approximations for the DFT-level geometries, revealing important steric interactions in some TSs. This combination of speed, accuracy, and mechanistic insight is unprecedented; current ML barrier models compromise on at least one of these important criteria.
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Affiliation(s)
- Elliot H E Farrar
- Department of Chemistry, University of Bath Claverton Down Bath BA2 7AY UK
| | - Matthew N Grayson
- Department of Chemistry, University of Bath Claverton Down Bath BA2 7AY UK
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14
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Laplaza R, Sobez JG, Wodrich MD, Reiher M, Corminboeuf C. The (not so) simple prediction of enantioselectivity - a pipeline for high-fidelity computations. Chem Sci 2022; 13:6858-6864. [PMID: 35774159 PMCID: PMC9200111 DOI: 10.1039/d2sc01714h] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 05/17/2022] [Indexed: 11/21/2022] Open
Abstract
The computation of reaction selectivity represents an appealing complementary route to experimental studies and a powerful means to refine catalyst design strategies. Accurately establishing the selectivity of reactions facilitated by molecular catalysts, however, remains a challenging task for computational chemistry. The small free energy differences that lead to large variations in the enantiomeric ratio (er) represent particularly tricky quantities to predict with sufficient accuracy to be helpful for prioritizing experiments. Further complicating this problem is the fact that standard approaches typically consider only one or a handful of conformers identified through human intuition as pars pro toto of the conformational space. Obviously, this assumption can potentially lead to dramatic failures should key energetic low-lying structures be missed. Here, we introduce a multi-level computational pipeline leveraging the graph-based Molassembler library to construct an ensemble of molecular catalysts. The manipulation and interpretation of molecules as graphs provides a powerful and direct route to tailored functionalization and conformer generation that facilitates high-throughput mechanistic investigations of chemical reactions. The capabilities of this approach are validated by examining a Rh(iii) catalyzed asymmetric C-H activation reaction and assessing the limitations associated with the underlying ligand design model. Specifically, the presence of remarkably flexible chiral Cp ligands, which induce the experimentally observed high level of selectivity, present a rich configurational landscape where multiple unexpected conformations contribute to the reported enantiomeric ratios (er). Using Molassembler, we show that considering about 20 transition state conformations per catalysts, which are generated with little human intervention and are not tied to "back-of-the-envelope" models, accurately reproduces experimental er values with limited computational expense.
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Affiliation(s)
- Rubén Laplaza
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Jan-Grimo Sobez
- Laboratorium für Physikalische Chemie, ETH Zürich Vladimir-Prelog-Weg 2 8093 Zürich Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), ETH Zürich Vladimir-Prelog-Weg 1-5/10 8093 Zürich Switzerland
| | - Matthew D Wodrich
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Markus Reiher
- Laboratorium für Physikalische Chemie, ETH Zürich Vladimir-Prelog-Weg 2 8093 Zürich Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), ETH Zürich Vladimir-Prelog-Weg 1-5/10 8093 Zürich Switzerland
| | - Clémence Corminboeuf
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
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15
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Quinn TR, Patel HN, Koh KH, Haines BE, Norrby PO, Helquist P, Wiest O. Automated fitting of transition state force fields for biomolecular simulations. PLoS One 2022; 17:e0264960. [PMID: 35271647 PMCID: PMC8912266 DOI: 10.1371/journal.pone.0264960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 02/22/2022] [Indexed: 12/29/2022] Open
Abstract
The generation of surrogate potential energy functions (PEF) that are orders of magnitude faster to compute but as accurate as the underlying training data from high-level electronic structure methods is one of the most promising applications of fitting procedures in chemistry. In previous work, we have shown that transition state force fields (TSFFs), fitted to the functional form of MM3* force fields using the quantum guided molecular mechanics (Q2MM) method, provide an accurate description of transition states that can be used for stereoselectivity predictions of small molecule reactions. Here, we demonstrate the applicability of the method for fit TSFFs to the well-established Amber force field, which could be used for molecular dynamics studies of enzyme reaction. As a case study, the fitting of a TSFF to the second hydride transfer in Pseudomonas mevalonii 3-hydroxy-3-methylglutaryl coenzyme A reductase (PmHMGR) is used. The differences and similarities to fitting of small molecule TSFFs are discussed.
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Affiliation(s)
- Taylor R. Quinn
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana, United States of America
- Early TDE Discovery, Early Oncology, Oncology R&D, AstraZeneca, Boston, Massachusetts, United States of America
| | - Himani N. Patel
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Kevin H. Koh
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Brandon E. Haines
- Department of Chemistry, Westmont College, Santa Barbara, California, United States of America
| | - Per-Ola Norrby
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, Mölndal, Sweden
| | - Paul Helquist
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana, United States of America
- Lab of Computational Chemistry and Drug Design, School of Chemical Biology and Biotechnology, Peking University, Shenzhen Graduate School, Shenzhen, China
- * E-mail:
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16
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Zhao Q, Hsu HH, Savoie BM. Conformational Sampling for Transition State Searches on a Computational Budget. J Chem Theory Comput 2022; 18:3006-3016. [PMID: 35403426 DOI: 10.1021/acs.jctc.2c00081] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Qiyuan Zhao
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Hsuan-Hao Hsu
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Brett M. Savoie
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
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17
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Cammarota RC, Liu W, Bacsa J, Davies HML, Sigman MS. Mechanistically Guided Workflow for Relating Complex Reactive Site Topologies to Catalyst Performance in C–H Functionalization Reactions. J Am Chem Soc 2022; 144:1881-1898. [DOI: 10.1021/jacs.1c12198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Ryan C. Cammarota
- Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States
| | - Wenbin Liu
- Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, Georgia 30322, United States
| | - John Bacsa
- Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, Georgia 30322, United States
| | - Huw M. L. Davies
- Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, Georgia 30322, United States
| | - Matthew S. Sigman
- Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States
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18
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Proofreading experimentally assigned stereochemistry through Q2MM predictions in Pd-catalyzed allylic aminations. Nat Commun 2021; 12:6719. [PMID: 34795274 PMCID: PMC8602308 DOI: 10.1038/s41467-021-27065-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/03/2021] [Indexed: 11/26/2022] Open
Abstract
The palladium-catalyzed enantioselective allylic substitution by carbon or nitrogen nucleophiles is a key transformation that is particularly useful for the synthesis of bioactive compounds. Unfortunately, the selection of a suitable ligand/substrate combination often requires significant screening effort. Here, we show that a transition state force field (TSFF) derived by the quantum-guided molecular mechanics (Q2MM) method can be used to rapidly screen ligand/substrate combinations. Testing of this method on 77 literature reactions revealed several cases where the computationally predicted major enantiomer differed from the one reported. Interestingly, experimental follow-up led to a reassignment of the experimentally observed configuration. This result demonstrates the power of mechanistically based methods to predict and, where necessary, correct the stereochemical outcome.
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19
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Okada H, Maeda S. A Dataset of Computational Reaction Barriers for the Claisen Rearrangement: Chemical and Numerical Analysis. Mol Inform 2021; 41:e2100216. [PMID: 34661976 DOI: 10.1002/minf.202100216] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 09/29/2021] [Indexed: 01/24/2023]
Abstract
Theoretical reaction screening based on Gibbs energy barriers would be promising to accelerate chemical reactions mining. The number of quantum chemical calculations can be reduced by using an optimization algorithm such as genetic algorithm (GA) and Bayesian optimization (BO). The focus of this study is to generate a dataset of reaction barriers of size ∼100000. Such a dataset would be useful to quickly evaluate various implementations of an optimization algorithm such as GA and BO. The dataset includes Gibbs energy barriers of the Claisen rearrangement for ∼100000 molecules computed on the basis of a semiempirical theory PM7. After evaluating its chemical and numerical features, it is found that the dataset well reflects chemical trends of various substitutions and is useful in testing various implementations of an optimization algorithm. The dataset is available in the supplementary material of this paper.
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Affiliation(s)
- Hiroaki Okada
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
| | - Satoshi Maeda
- Department of Chemistry, Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan.,Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan.,ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan.,Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
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20
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Nandy A, Duan C, Taylor MG, Liu F, Steeves AH, Kulik HJ. Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning. Chem Rev 2021; 121:9927-10000. [PMID: 34260198 DOI: 10.1021/acs.chemrev.1c00347] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.
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Affiliation(s)
- Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Michael G Taylor
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Fang Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Adam H Steeves
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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21
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Wan Z, Wang QD, Liu D, Liang J. Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors. Org Biomol Chem 2021; 19:6267-6273. [PMID: 34195743 DOI: 10.1039/d1ob01066b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge and computer predictions. In this work, a new scenario, which combines a data-driven machine learning (ML) model with reactivity descriptors, is developed to predict the optimal enzyme-catalyzed synthesis conditions and the reaction yield. Fourteen reactivity descriptors in total are constructed to describe 125 reactions (classified into five categories) included in different reaction mechanisms. Nineteen ML models are developed to train the dataset and the Quadratic support vector machine (SVM) model is found to exhibit the best performance. The Quadratic SVM model is then used to predict the optimal reaction conditions, which are subsequently used to obtain the highest yield among 109 200 reaction conditions with different molar ratios of substrates, solvents, water contents, enzyme concentrations and temperatures for each reaction. The proposed protocol should be generally applicable to a diverse range of chemical reactions and provides a black-box evaluation for optimizing the reaction conditions of organic synthesis reactions.
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Affiliation(s)
- Zhongyu Wan
- Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, Low Carbon Energy Institute and School of Chemical Engineering, China University of Mining and Technology, Xuzhou, 221008, People's Republic of China. and School of Science, City University of Hong Kong, Hong Kong SAR 999077, People's Republic of China
| | - Quan-De Wang
- Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, Low Carbon Energy Institute and School of Chemical Engineering, China University of Mining and Technology, Xuzhou, 221008, People's Republic of China.
| | - Dongchang Liu
- School of Science, Xi'an Polytechnic University, Xi'an 710048, People's Republic of China and Department of Physics, Sungkyunkwan University, Suwon 16419, Korea
| | - Jinhu Liang
- School of Environment and Safety Engineering, North University of China, Taiyuan 030051, People's Republic of China
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22
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Maeda S, Harabuchi Y. Exploring paths of chemical transformations in molecular and periodic systems: An approach utilizing force. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1538] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI‐ICReDD), Hokkaido University Sapporo Hokkaido Japan
- Department of Chemistry, Faculty of Science Hokkaido University Sapporo Hokkaido Japan
- JST, ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project Sapporo Hokkaido Japan
- National Institute for Materials Science (NIMS) Research and Services Division of Materials Data and Integrated System (MaDIS) Tsukuba Ibaraki Japan
| | - Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI‐ICReDD), Hokkaido University Sapporo Hokkaido Japan
- Department of Chemistry, Faculty of Science Hokkaido University Sapporo Hokkaido Japan
- JST, ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project Sapporo Hokkaido Japan
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23
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Zahrt AF, Rinehart NI, Denmark SE. A Conformer‐Dependent, Quantitative Quadrant Model. European J Org Chem 2021. [DOI: 10.1002/ejoc.202100027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Andrew F. Zahrt
- Roger Adams Laboratory Department of Chemistry University of Illinois 600 S. Mathews Ave Urbana, IL 61801 USA
| | - N. Ian Rinehart
- Roger Adams Laboratory Department of Chemistry University of Illinois 600 S. Mathews Ave Urbana, IL 61801 USA
| | - Scott E. Denmark
- Roger Adams Laboratory Department of Chemistry University of Illinois 600 S. Mathews Ave Urbana, IL 61801 USA
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24
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Wahlers J, Maloney M, Salahi F, Rosales AR, Helquist P, Norrby PO, Wiest O. Stereoselectivity Predictions for the Pd-Catalyzed 1,4-Conjugate Addition Using Quantum-Guided Molecular Mechanics. J Org Chem 2021; 86:5660-5667. [PMID: 33769065 DOI: 10.1021/acs.joc.1c00136] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The conjugate addition of aryl boronic acids to enones is a powerful synthetic tool to introduce quaternary chiral centers, but the experimentally observed stereoselectivities vary widely, and the identification of suitable substrate-ligand combinations requires significant effort. We describe the development and application of a transition-state force field (TSFF) by the quantum-guided molecular mechanics (Q2MM) method that is validated using an automated screen of 9 ligands, 38 aryl boronic acids, and 22 enones, leading to a MUE of 1.8 kJ/mol and a R2 value of 0.877 over 82 examples. A detailed error analysis identified the structural origin for the deviations in the small group of outliers. The TSFF was then used to predict the stereoselectivity for 27 ligands and 59 enones. The vast majority of the virtual screening results are in line with the expected results. Selected results for 6-substituted pyrox ligands, which were not part of the training set, were followed up by density functional theory and experimental studies.
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Affiliation(s)
- Jessica Wahlers
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Michael Maloney
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Farbod Salahi
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Anthony R Rosales
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Paul Helquist
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Per-Ola Norrby
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden.,Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
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25
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Gallarati S, Fabregat R, Laplaza R, Bhattacharjee S, Wodrich MD, Corminboeuf C. Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts. Chem Sci 2021; 12:6879-6889. [PMID: 34123316 PMCID: PMC8153079 DOI: 10.1039/d1sc00482d] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/01/2021] [Indexed: 12/12/2022] Open
Abstract
Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol-1 were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.
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Affiliation(s)
- Simone Gallarati
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Raimon Fabregat
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Rubén Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Sinjini Bhattacharjee
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- Indian Institute of Science Education and Research Dr Homi Bhabha Rd, Ward No. 8, NCL Colony, Pashan Pune Maharashtra 411008 India
| | - Matthew D Wodrich
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Clemence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
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26
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Quinn TR, Steussy CN, Haines BE, Lei J, Wang W, Sheong FK, Stauffacher CV, Huang X, Norrby PO, Helquist P, Wiest O. Microsecond timescale MD simulations at the transition state of PmHMGR predict remote allosteric residues. Chem Sci 2021; 12:6413-6418. [PMID: 34084441 PMCID: PMC8115266 DOI: 10.1039/d1sc00102g] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Understanding the mechanisms of enzymatic catalysis requires a detailed understanding of the complex interplay of structure and dynamics of large systems that is a challenge for both experimental and computational approaches. More importantly, the computational demands of QM/MM simulations mean that the dynamics of the reaction can only be considered on a timescale of nanoseconds even though the conformational changes needed to reach the catalytically active state happen on a much slower timescale. Here we demonstrate an alternative approach that uses transition state force fields (TSFFs) derived by the quantum-guided molecular mechanics (Q2MM) method that provides a consistent treatment of the entire system at the classical molecular mechanics level and allows simulations at the microsecond timescale. Application of this approach to the second hydride transfer transition state of HMG-CoA reductase from Pseudomonas mevalonii (PmHMGR) identified three remote residues, R396, E399 and L407, (15-27 Å away from the active site) that have a remote dynamic effect on enzyme activity. The predictions were subsequently validated experimentally via site-directed mutagenesis. These results show that microsecond timescale MD simulations of transition states are possible and can predict rather than just rationalize remote allosteric residues.
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Affiliation(s)
- Taylor R Quinn
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA .,Early TDE Discovery, Early Oncology, Oncology R&D, AstraZeneca Boston USA
| | - Calvin N Steussy
- Department of Biological Sciences, Purdue Center for Cancer Research, Purdue University West Lafayette IN 47907 USA
| | - Brandon E Haines
- Department of Chemistry, Westmont College Santa Barbara CA 93108 USA
| | - Jinping Lei
- Department of Chemistry, The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China.,School of Pharmaceutical Sciences, Sun Yat-sen University Guangzhou 510006 China
| | - Wei Wang
- Department of Chemistry, The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China
| | - Cynthia V Stauffacher
- Department of Biological Sciences, Purdue Center for Cancer Research, Purdue University West Lafayette IN 47907 USA
| | - Xuhui Huang
- Department of Chemistry, The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China
| | - Per-Ola Norrby
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA .,Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg Pepparedsleden 1 SE-431 83 Mölndal Sweden
| | - Paul Helquist
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA .,Lab of Computational Chemistry and Drug Design, School of Chemical Biology and Biotechnology, Peking University, Shenzhen Graduate School Shenzhen China
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27
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Abstract
As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.
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28
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Durand DJ, Fey N. Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis. Acc Chem Res 2021; 54:837-848. [PMID: 33533587 DOI: 10.1021/acs.accounts.0c00807] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Computers have become closely involved with most aspects of modern life, and these developments are tracked in the chemical sciences. Recent years have seen the integration of computing across chemical research, made possible by investment in equipment, software development, improved networking between researchers, and rapid growth in the application of predictive approaches to chemistry, but also a change of attitude rooted in the successes of computational chemistry-it is now entirely possible to complete research projects where computation and synthesis are cooperative and integrated, and work in synergy to achieve better insights and improved results. It remains our ambition to put computational prediction before experiment, and we have been working toward developing the key ingredients and workflows to achieve this.The ability to precisely tune selectivity along with high catalyst activity make organometallic catalysts using transition metal (TM) centers ideal for high-value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalyzed reactions across the vast chemical space of different catalysts and substrates are not fully explored, and such an exploration is not feasible with current resources. This can lead to complete synthetic failures when new substrates are used, but more commonly we see outcomes that require further optimization, such as incomplete conversion, insufficient selectivity, or the appearance of unwanted side products. These processes consume time and resources, but the insights and data generated are usually not tied to a broader predictive workflow where experiments test hypotheses quantitatively, reducing their impact.These failures suggest at least a partial deviation of the reaction pathway from that hypothesized, hinting at quite complex mechanistic manifolds for organometallic catalysts that are affected by the combination of input variables. Mechanistic deviation is most likely when challenging multifunctional substrates are being used, and the quest for so-called privileged catalysts is quickly replaced by a need to screen catalyst libraries until a new "best" match between the catalyst and substrate can be identified and the reaction conditions can be optimized. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealized catalytic cycles rather than working toward a "big data" view of organometallic homogeneous catalysis with routine use of predictive models and transparent data sharing.Databases of DFT-calculated steric and electronic descriptors can be built for such catalysts, and we summarize here how these can be used in the mapping, interpretation, and prediction of catalyst properties and reactivities. Our motivation is to make these databases useful as tools for synthetic chemists so that they challenge and validate quantitative computational approaches. In this Account, we demonstrate their application to different aspects of catalyst design and discovery and their integration with computational mechanistic studies and thus describe the progress of our journey toward truly predictive models in homogeneous organometallic catalysis.
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Affiliation(s)
- Derek J. Durand
- School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, U.K
| | - Natalie Fey
- School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, U.K
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29
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Sun G, Fuller JT, Alexandrova AN, Sautet P. Global Activity Search Uncovers Reaction Induced Concomitant Catalyst Restructuring for Alkane Dissociation on Model Pt Catalysts. ACS Catal 2021. [DOI: 10.1021/acscatal.0c05421] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Geng Sun
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Jack T. Fuller
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Anastassia N. Alexandrova
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
- California Nano Systems Institute, Los Angeles, California 90095-1569, United States
| | - Philippe Sautet
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
- California Nano Systems Institute, Los Angeles, California 90095-1569, United States
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30
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Besora M, Maseras F. Computational insights into metal-catalyzed asymmetric hydrogenation. ADVANCES IN CATALYSIS 2021. [DOI: 10.1016/bs.acat.2021.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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31
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Urabe D, Fukaya K. Systematic Search for Transition States in Complex Molecules: Computational Analyses of Regio- and Stereoselective Interflavan Bond Formation in Flavan-3-ols. HETEROCYCLES 2021. [DOI: 10.3987/rev-20-943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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32
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Zahrt AF, Rose BT, Darrow WT, Henle JJ, Denmark SE. Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next. REACT CHEM ENG 2021. [DOI: 10.1039/d1re00013f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from in silico libraries of catalyst structures.
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Affiliation(s)
- Andrew F. Zahrt
- 245 Roger Adams Laboratory
- Department of Chemistry
- University of Illinois
- Urbana
- USA
| | - Brennan T. Rose
- 245 Roger Adams Laboratory
- Department of Chemistry
- University of Illinois
- Urbana
- USA
| | - William T. Darrow
- 245 Roger Adams Laboratory
- Department of Chemistry
- University of Illinois
- Urbana
- USA
| | - Jeremy J. Henle
- 245 Roger Adams Laboratory
- Department of Chemistry
- University of Illinois
- Urbana
- USA
| | - Scott E. Denmark
- 245 Roger Adams Laboratory
- Department of Chemistry
- University of Illinois
- Urbana
- USA
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33
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Moon S, Chatterjee S, Seeberger PH, Gilmore K. Predicting glycosylation stereoselectivity using machine learning. Chem Sci 2020; 12:2931-2939. [PMID: 34164060 PMCID: PMC8179398 DOI: 10.1039/d0sc06222g] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 12/24/2020] [Indexed: 12/15/2022] Open
Abstract
Predicting the stereochemical outcome of chemical reactions is challenging in mechanistically ambiguous transformations. The stereoselectivity of glycosylation reactions is influenced by at least eleven factors across four chemical participants and temperature. A random forest algorithm was trained using a highly reproducible, concise dataset to accurately predict the stereoselective outcome of glycosylations. The steric and electronic contributions of all chemical reagents and solvents were quantified by quantum mechanical calculations. The trained model accurately predicts stereoselectivities for unseen nucleophiles, electrophiles, acid catalyst, and solvents across a wide temperature range (overall root mean square error 6.8%). All predictions were validated experimentally on a standardized microreactor platform. The model helped to identify novel ways to control glycosylation stereoselectivity and accurately predicts previously unknown means of stereocontrol. By quantifying the degree of influence of each variable, we begin to gain a better general understanding of the transformation, for example that environmental factors influence the stereoselectivity of glycosylations more than the coupling partners in this area of chemical space.
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Affiliation(s)
- Sooyeon Moon
- Department of Biomolecular Systems, Max-Planck-Institute of Colloids and Interfaces Am Mühlenberg 1 14476 Potsdam Germany
- Freie Universität Berlin, Institute of Chemistry and Biochemistry Arnimallee 22 14195 Berlin Germany
| | - Sourav Chatterjee
- Department of Biomolecular Systems, Max-Planck-Institute of Colloids and Interfaces Am Mühlenberg 1 14476 Potsdam Germany
| | - Peter H Seeberger
- Department of Biomolecular Systems, Max-Planck-Institute of Colloids and Interfaces Am Mühlenberg 1 14476 Potsdam Germany
- Freie Universität Berlin, Institute of Chemistry and Biochemistry Arnimallee 22 14195 Berlin Germany
| | - Kerry Gilmore
- Department of Biomolecular Systems, Max-Planck-Institute of Colloids and Interfaces Am Mühlenberg 1 14476 Potsdam Germany
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34
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Neto DHC, Dos Santos AAM, Da Silva JCS, Rocha WR, Dias RP. Propene Hydroformylation Reaction Catalyzed by HRh(CO)(BISBI): A Thermodynamic and Kinetic Analysis of the Full Catalytic Cycle. Eur J Inorg Chem 2020. [DOI: 10.1002/ejic.202000799] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Daniel H. Cruz Neto
- Faculté des Sciences d'Orsay UFR Sciences Université Paris‐Saclay 91400 Orsay Île‐de‐France France
| | - Artur A. M. Dos Santos
- LQCBIO: Laboratório de Química Computacional e Modelagem de Biomoléculas Instituto de Química e Biotecnologia, IQB Universidade Federal de Alagoas Campus A. C. Simões 57072‐900 Maceió AL Brazil
| | - Júlio C. S. Da Silva
- LQCBIO: Laboratório de Química Computacional e Modelagem de Biomoléculas Instituto de Química e Biotecnologia, IQB Universidade Federal de Alagoas Campus A. C. Simões 57072‐900 Maceió AL Brazil
- eCsMoLab: Laboratório de Estudos Computacionais em Sistemas Moleculares Departamento de Química, ICEx Universidade Federal de Minas Gerais 31270‐901 Pampulha Belo Horizonte, MG Brazil
| | - Willian R. Rocha
- eCsMoLab: Laboratório de Estudos Computacionais em Sistemas Moleculares Departamento de Química, ICEx Universidade Federal de Minas Gerais 31270‐901 Pampulha Belo Horizonte, MG Brazil
| | - Roberta P. Dias
- eCsMoLab: Laboratório de Estudos Computacionais em Sistemas Moleculares Departamento de Química, ICEx Universidade Federal de Minas Gerais 31270‐901 Pampulha Belo Horizonte, MG Brazil
- GIMMM: Grupo Interdisciplinar de Modelagem Molecular e Simulação de Materiais Núcleo Interdisciplinar de Ciências Exatas e Inovação Tecnológica ‐ NICEN, Campus do Agreste Universidade Federal de Pernambuco 55002‐970 Caruaru PE Brazil
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35
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Singh A, Chayawan, Mehta SK, Singh K. Catalyst free enantioselective amination via S N2 nucleophilic substitution reaction: a computational study. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1795167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Amritpal Singh
- School of Chemistry, Faculty of Basic Sciences, Shoolini University, Solan, India
| | - Chayawan
- Mario Negri Istituto di ricerche farmacologiche, Milan, Italy
| | - S. K. Mehta
- Department of Chemistry, Panjab University, Chandigarh, India
| | - Kulvinder Singh
- Department of Chemistry, School of Basic and Applied Sciences, Maharaja Agrasen University, Baddi, India
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36
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Lam YH, Abramov Y, Ananthula RS, Elward JM, Hilden LR, Nilsson Lill SO, Norrby PO, Ramirez A, Sherer EC, Mustakis J, Tanoury GJ. Applications of Quantum Chemistry in Pharmaceutical Process Development: Current State and Opportunities. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00222] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yu-hong Lam
- Computational and Structural Chemistry, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Yuriy Abramov
- Pfizer Worldwide Research & Development, Groton, Connecticut 06340, United States
| | - Ravi S. Ananthula
- Small Molecule Design and Development, Eli Lilly and Company, Bangalore 560103, India
| | - Jennifer M. Elward
- Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Collegeville, Pennsylvania 19426, United States
| | - Lori R. Hilden
- Small Molecule Design and Development, Eli Lilly and Company, Indianapolis, Indiana 46221, United States
| | - Sten O. Nilsson Lill
- Early Product Development and Manufacturing, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, Mölndal 431 50, Sweden
| | - Per-Ola Norrby
- Data Science & Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, Mölndal 431 50 Sweden
| | - Antonio Ramirez
- Chemical and Synthetic Development, Bristol-Myers Squibb Company, One Squibb Drive, New Brunswick, New Jersey 08903, United States
| | - Edward C. Sherer
- Computational and Structural Chemistry, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Jason Mustakis
- Pfizer Worldwide Research & Development, Groton, Connecticut 06340, United States
| | - Gerald J. Tanoury
- Process Chemistry, Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
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37
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Burai Patrascu M, Pottel J, Pinus S, Bezanson M, Norrby PO, Moitessier N. From desktop to benchtop with automated computational workflows for computer-aided design in asymmetric catalysis. Nat Catal 2020. [DOI: 10.1038/s41929-020-0468-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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38
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Henle JJ, Zahrt AF, Rose BT, Darrow WT, Wang Y, Denmark SE. Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis. J Am Chem Soc 2020; 142:11578-11592. [PMID: 32568531 DOI: 10.1021/jacs.0c04715] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.
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Affiliation(s)
- Jeremy J Henle
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
| | - Andrew F Zahrt
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
| | - Brennan T Rose
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
| | - William T Darrow
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
| | - Yang Wang
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
| | - Scott E Denmark
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
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39
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Rosales AR, Ross SP, Helquist P, Norrby PO, Sigman MS, Wiest O. Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction. J Am Chem Soc 2020; 142:9700-9707. [PMID: 32249569 DOI: 10.1021/jacs.0c01979] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
A transition state force field (TSFF) was developed using the quantum-guided molecular mechanics (Q2MM) method to describe the stereodetermining migratory insertion step of the enantioselective redox-relay Heck reaction for a range of multisubstituted alkenes. We show that the TSFF is highly predictive through an external validation of the TSFF against 151 experimentally determined stereoselectivities resulting in an R2 of 0.89 and MUE of 1.8 kJ/mol. In addition, limitations in the underlying force field were identified by comparison of the TSFF results to DFT level calculations. A novel application of the TSFF was demonstrated for 31 cases where the enantiomer predicted by the TSFF differed from the originally published values. Experimental determination of the absolute configuration demonstrated that the computational predictions were accurate, suggesting that TSFFs can be used for the rapid prediction of the absolute stereochemistry for a class of reactions. Finally, a virtual ligand screen was conducted utilizing both the TSFF and a simple molecular correlation method. Both methods were similarly predictive, but the TSFF was able to show greater utility through transferability, speed, and interpretability.
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Affiliation(s)
- Anthony R Rosales
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Sean P Ross
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Paul Helquist
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Per-Ola Norrby
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, SE-43183 Mölndal, Sweden
| | - Matthew S Sigman
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States.,Lab of Computational Chemistry and Drug Design, School of Chemical Biology and Biotechnology, Peking University, Shenzhen Graduate School, Shenzhen, China
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40
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Yang Y, Zhang X, Zhong LP, Lan J, Li X, Li CC, Chung LW. Unusual KIE and dynamics effects in the Fe-catalyzed hetero-Diels-Alder reaction of unactivated aldehydes and dienes. Nat Commun 2020; 11:1850. [PMID: 32296076 PMCID: PMC7160212 DOI: 10.1038/s41467-020-15599-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 03/13/2020] [Indexed: 12/30/2022] Open
Abstract
Hetero-Diels-Alder (HDA) reaction is an important synthetic method for many natural products. An iron(III) catalyst was developed to catalyze the challenging HDA reaction of unactivated aldehydes and dienes with high selectivity. Here we report extensive density-functional theory (DFT) calculations and molecular dynamics simulations that show effects of iron (including its coordinate mode and/or spin state) on the dynamics of this reaction: considerably enhancing dynamically stepwise process, broadening entrance channel and narrowing exit channel from concerted asynchronous transition states. Also, our combined computational and experimental secondary KIE studies reveal unexpectedly large KIE values for the five-coordinate pathway even with considerable C-C bond forming, due to equilibrium isotope effect from the change in the metal coordination. Moreover, steric and electronic effects are computationally shown to dictate the C=O chemoselectivity for an α,β-unsaturated aldehyde, which is verified experimentally. Our mechanistic study may help design homogeneous, heterogeneous and biological catalysts for this challenging reaction.
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Affiliation(s)
- Yuhong Yang
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalytic Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Xiaoyong Zhang
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalytic Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Li-Ping Zhong
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalytic Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Jialing Lan
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalytic Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Xin Li
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalytic Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Chuang-Chuang Li
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalytic Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Lung Wa Chung
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalytic Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China.
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41
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Zahrt AF, Athavale SV, Denmark SE. Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future. Chem Rev 2020; 120:1620-1689. [PMID: 31886649 PMCID: PMC7018559 DOI: 10.1021/acs.chemrev.9b00425] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.
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Affiliation(s)
- Andrew F. Zahrt
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801
| | - Soumitra V. Athavale
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801
| | - Scott E. Denmark
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801
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42
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Affiliation(s)
- Marco Foscato
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
| | - Vidar R. Jensen
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
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43
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Ligand Design for Asymmetric Catalysis: Combining Mechanistic and Chemoinformatics Approaches. TOP ORGANOMETAL CHEM 2020. [DOI: 10.1007/3418_2020_47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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44
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Artificial Force-Induced Reaction Method for Systematic Elucidation of Mechanism and Selectivity in Organometallic Reactions. TOP ORGANOMETAL CHEM 2020. [DOI: 10.1007/3418_2020_51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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45
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Reid JP, Sigman MS. Holistic prediction of enantioselectivity in asymmetric catalysis. Nature 2019; 571:343-348. [PMID: 31316193 PMCID: PMC6641578 DOI: 10.1038/s41586-019-1384-z] [Citation(s) in RCA: 151] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 05/29/2019] [Indexed: 12/24/2022]
Abstract
When faced with unfamiliar reaction space, synthetic chemists typically apply reported conditions (reagents, catalyst, solvent, additives) from closely-related reactions to new substrate types. Unfortunately, this approach often fails due to subtle, albeit important, differences in reaction requirements. Consequently, a significant goal in synthetic chemistry is the ability to transfer chemical observations from one reaction to another, quantitatively. Here, we present such a platform by developing a holistic, data-driven workflow for deriving statistical models for one set of reactions that can be applied to predict out-of-sample examples. As a validating case study, published enantioselectivity data sets that employ BINOL-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines were combined and statistical models developed. These models reveal the general interactions imparting asymmetric induction and allow the quantitative transfer of this information to new reaction components. The disclosed techniques create opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development. After the database of the reactions is constructed, the experimental output, enantiomeric ratios, were mathematically modelled through linear regression techniques to reveal which of the proposed parameters allow for the prediction of new outcomes. The detailed acquisition of parameters can be found in the Supplementary Information and the descriptor tables attached as an accompanying spreadsheet. The models produced were evaluated for their goodness of fit, R2, and their robustness is demonstrated by external validations’ goodness of fit, predR2. The nearer the R2 and slope values are to one (indicating a tight, one-to-one correlation between predicted and measured outcomes) and the nearer the intercept is to zero (indicating minimal systematic error), the more robust the model. Potential models were refined through number of parameters, because this allows for a mechanistically informative interrogation and cross-validation scores. Leave one reaction out (LORO) analysis was performed to probe general mechanistic principles, which provides the basis for mechanistic transfer of experimental observations and tested further by predicting out-of-sample.
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Affiliation(s)
- Jolene P Reid
- Department of Chemistry, University of Utah, Salt Lake City, UT, USA
| | - Matthew S Sigman
- Department of Chemistry, University of Utah, Salt Lake City, UT, USA.
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46
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Daubignard J, Lutz M, Detz RJ, de Bruin B, Reek JNH. Origin of the Selectivity and Activity in the Rhodium-Catalyzed Asymmetric Hydrogenation Using Supramolecular Ligands. ACS Catal 2019. [DOI: 10.1021/acscatal.9b01809] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Julien Daubignard
- Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, Netherlands
| | - Martin Lutz
- Crystal and Structural Chemistry Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht 3584 CH, Netherlands
| | - Remko J. Detz
- Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, Netherlands
| | - Bas de Bruin
- Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, Netherlands
| | - Joost N. H. Reek
- Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, Netherlands
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Seo CSG, Morris RH. Catalytic Homogeneous Asymmetric Hydrogenation: Successes and Opportunities. Organometallics 2018. [DOI: 10.1021/acs.organomet.8b00774] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Chris S. G. Seo
- Department of Chemistry, University of Toronto, M5S3H6 Toronto, Ontario, Canada
| | - Robert H. Morris
- Department of Chemistry, University of Toronto, M5S3H6 Toronto, Ontario, Canada
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Comparing quantitative prediction methods for the discovery of small-molecule chiral catalysts. Nat Rev Chem 2018. [DOI: 10.1038/s41570-018-0040-8] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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