1
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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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2
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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. [PMID: 38995078 DOI: 10.1021/acs.jcim.4c00370] [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
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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3
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Mészáros BB, Kubicskó K, Németh DD, Daru J. Emerging Conformational-Analysis Protocols from the RTCONF55-16K Reaction Thermochemistry Conformational Benchmark Set. J Chem Theory Comput 2024. [PMID: 38899777 DOI: 10.1021/acs.jctc.4c00565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
RTCONF55-16K is a new, reactive conformational data set based on cost-efficient methods to assess different conformational analysis protocols. Our reference calculations underpinned the accuracy of the CENSO (Grimme et al. J. Phys. Chem. A, 2021, 125, 4039) procedure and resulted in alternative recipes with different cost-accuracy compromises. Our general-purpose and economical protocols (CENSO-light and zero, respectively) were found to be 10-30 times faster than the original algorithm, adding only 0.4-0.7 kcal/mol absolute error to the relative free energy estimates.
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Affiliation(s)
- Bence Balázs Mészáros
- Hevesy György PhD School of Chemistry, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
- Department of Organic Chemistry, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
| | - Károly Kubicskó
- Hevesy György PhD School of Chemistry, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
- Department of Organic Chemistry, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
| | - Dávid Dorián Németh
- Department of Organic Chemistry, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
| | - János Daru
- Department of Organic Chemistry, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
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4
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Milon TI, Wang Y, Fontenot RL, Khajouie P, Villinger F, Raghavan V, Xu W. Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions. Comput Biol Chem 2024; 112:108117. [PMID: 38852360 DOI: 10.1016/j.compbiolchem.2024.108117] [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: 03/05/2024] [Revised: 05/13/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.
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Affiliation(s)
- Tarikul I Milon
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Yuhong Wang
- National Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ryan L Fontenot
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Poorya Khajouie
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA; The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA
| | - Francois Villinger
- Department of Biology, University of Louisiana at Lafayette, New Iberia, LA 70560, USA
| | - Vijay Raghavan
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA
| | - Wu Xu
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
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5
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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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6
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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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7
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Afzal M, Qais FA, Abduh NA, Christy M, Ayub R, Alarifi A. Identification of bioactive compounds of Zanthoxylum armatum as potential inhibitor of pyruvate kinase M2 (PKM2): Computational and virtual screening approaches. Heliyon 2024; 10:e27361. [PMID: 38495183 PMCID: PMC10943388 DOI: 10.1016/j.heliyon.2024.e27361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024] Open
Abstract
PKM2 (Pyruvate kinase M2) is the isoform of pyruvate kinase which is known to catalyse the last step of glycolysis that is responsible for energy production. This specific isoform is known to be highly expressed in certain cancerous conditions. Considering the role of this protein in various cancer conditions, we used PKM2 as a target protein to identify the potential compounds against this target. In this study, we have examined 96 compounds of Zanthoxylum armatum using an array of computational and in silico tools. The compounds were assessed for toxicity then their anticancer potential was predicted. The virtual screening was done with molecular docking followed by a detailed examination using molecular dynamics simulation. The majority of the compounds showed a higher probability of being antineoplastic. Based on toxicity, predicted anticancer potential, binding affinity, and binding site, three compounds (nevadensin, asarinin, and kaempferol) were selected as hit compounds. The binding energy of these compounds with PKM2 ranged from -7.7 to -8.3 kcal/mol and all hit compounds interact at the active site of the protein. The selected hit compounds formed a stable complex with PKM2 when simulated under physiological conditions. The dynamic analysis showed that these compounds remained attached to the active site till the completion of molecular simulation. MM-PBSA analysis showed that nevadensin exhibited a higher affinity towards PKM2 compared to asarinin and kaempferol. These compounds need to be assessed properties in vivo and in vitro to validate their efficacy.
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Affiliation(s)
- Mohd Afzal
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Faizan Abul Qais
- Department of Agricultural Microbiology, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh, UP, 202002, India
| | - Naaser A.Y. Abduh
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Maria Christy
- Department of Energy Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Rashid Ayub
- Department of Science Technology and Innovation, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Abdullah Alarifi
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
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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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Zhou X, Wang Z, Li S, Rong X, Bu J, Liu Q, Ouyang Z. Differentiating enantiomers by directional rotation of ions in a mass spectrometer. Science 2024; 383:612-618. [PMID: 38330101 DOI: 10.1126/science.adj8342] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 01/03/2024] [Indexed: 02/10/2024]
Abstract
Conventional mass spectrometry does not distinguish between enantiomers, or mirror-image isomers. Here we report a technique to break the chiral symmetry and to differentiate enantiomers by inducing directional rotation of chiral gas-phase ions. Dual alternating current excitations were applied to manipulate the motions of trapped ions, including the rotation around the center of mass and macro movement around the center of the trap. Differences in collision cross section were induced, which could be measured by ion cloud profiling at high resolutions above 10,000. High-field ion mobility and tandem mass spectrometry analyses of the enantiomers were combined and implemented by using a miniature ion trap mass spectrometer. The effectiveness of the developed method was demonstrated with a variety of organic compounds including amino acids, sugars, and several drug molecules, as well as a proof-of-principle ligand optimization study for asymmetric hydrogenation.
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Affiliation(s)
- Xiaoyu Zhou
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Zhuofan Wang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Shuai Li
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Xianle Rong
- Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Jiexun Bu
- PURSPEC Technology (Beijing) Ltd., Beijing 100084, China
| | - Qiang Liu
- Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Zheng Ouyang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
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10
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Foscato M, Occhipinti G, Hopen Eliasson SH, Jensen VR. Automated de Novo Design of Olefin Metathesis Catalysts: Computational and Experimental Analysis of a Simple Thermodynamic Design Criterion. J Chem Inf Model 2024; 64:412-424. [PMID: 38247361 PMCID: PMC10806812 DOI: 10.1021/acs.jcim.3c01649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/14/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Methods for computational de novo design of inorganic molecules have paved the way for automated design of homogeneous catalysts. Such studies have so far relied on correlation-based prediction models as fitness functions (figures of merit), but the soundness of these approaches has yet to be tested by experimental verification of de novo-designed catalysts. Here, a previously developed criterion for the optimization of dative ligands L in ruthenium-based olefin metathesis catalysts RuCl2(L)(L')(═CHAr), where Ar is an aryl group and L' is a phosphine ligand dissociating to activate the catalyst, was used in de novo design experiments. These experiments predicted catalysts bearing an N-heterocyclic carbene (L = 9) substituted by two N-bound mesityls and two tert-butyl groups at the imidazolidin-2-ylidene backbone to be promising. Whereas the phosphine-stabilized precursor assumed by the prediction model could not be made, a pyridine-stabilized ruthenium alkylidene complex (17) bearing carbene 9 was less active than a known leading pyridine-stabilized Grubbs-type catalyst (18, L = H2IMes). A density functional theory-based analysis showed that the unsubstituted metallacyclobutane (MCB) intermediate generated in the presence of ethylene is the likely resting state of both 17 and 18. Whereas the design criterion via its correlation between the stability of the MCB and the rate-determining barrier indeed seeks to stabilize the MCB, it relies on RuCl2(L)(L')(═CH2) adducts as resting states. The change in resting state explains the discrepancy between the prediction and the actual performance of catalyst 17. To avoid such discrepancies and better address the multifaceted challenges of predicting catalytic performance, future de novo catalyst design studies should explore and test design criteria incorporating information from more than a single relative energy or intermediate.
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Affiliation(s)
- Marco Foscato
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
| | - Giovanni Occhipinti
- 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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11
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Jabbour R, Ashling CW, Robinson TC, Khan AH, Wisser D, Berruyer P, Ghosh AC, Ranscht A, Keen DA, Brunner E, Canivet J, Bennett TD, Mellot-Draznieks C, Lesage A, Wisser FM. Unravelling the Molecular Structure and Confining Environment of an Organometallic Catalyst Heterogenized within Amorphous Porous Polymers. Angew Chem Int Ed Engl 2023; 62:e202310878. [PMID: 37647152 DOI: 10.1002/anie.202310878] [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: 07/28/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 09/01/2023]
Abstract
The catalytic activity of multifunctional, microporous materials is directly linked to the spatial arrangement of their structural building blocks. Despite great achievements in the design and incorporation of isolated catalytically active metal complexes within such materials, a detailed understanding of their atomic-level structure and the local environment of the active species remains a fundamental challenge, especially when these latter are hosted in non-crystalline organic polymers. Here, we show that by combining computational chemistry with pair distribution function analysis, 129 Xe NMR, and Dynamic Nuclear Polarization enhanced NMR spectroscopy, a very accurate description of the molecular structure and confining surroundings of a catalytically active Rh-based organometallic complex incorporated inside the cavity of amorphous bipyridine-based porous polymers is obtained. Small, but significant, differences in the structural properties of the polymers are highlighted depending on their backbone motifs.
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Affiliation(s)
- Ribal Jabbour
- Centre de RMN à Très Hauts Champs, Université de Lyon (CNRS/ENS Lyon/UCB Lyon 1), 69100, Villeurbanne, France
| | - Christopher W Ashling
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge, CB3 0FS, UK
| | - Thomas C Robinson
- Centre de RMN à Très Hauts Champs, Université de Lyon (CNRS/ENS Lyon/UCB Lyon 1), 69100, Villeurbanne, France
| | - Arafat Hossain Khan
- Chair of Bioanalytical Chemistry, TU Dresden, Bergstraße 66, 01069, Dresden, Germany
| | - Dorothea Wisser
- Erlangen Center for Interface Research and Catalysis (ECRC), Friedrich-Alexander-Universität Erlangen-Nürnberg, Egerlandstraße 3, 91058, Erlangen, Germany
| | - Pierrick Berruyer
- Centre de RMN à Très Hauts Champs, Université de Lyon (CNRS/ENS Lyon/UCB Lyon 1), 69100, Villeurbanne, France
| | - Ashta C Ghosh
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON - UMR 5256, 2 Avenue Albert Einstein, 69626, Villeurbanne Cedex, France
| | - Alisa Ranscht
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON - UMR 5256, 2 Avenue Albert Einstein, 69626, Villeurbanne Cedex, France
| | - David A Keen
- ISIS Facility, Rutherford Appleton Laboratory, Harwell Campus, Didcot, Oxfordshire, OX11 0QX, UK
| | - Eike Brunner
- Chair of Bioanalytical Chemistry, TU Dresden, Bergstraße 66, 01069, Dresden, Germany
| | - Jérôme Canivet
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON - UMR 5256, 2 Avenue Albert Einstein, 69626, Villeurbanne Cedex, France
| | - Thomas D Bennett
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge, CB3 0FS, UK
| | - Caroline Mellot-Draznieks
- Laboratoire de Chimie des Processus Biologiques (LCPB), Collège de France, PSL Research University, CNRS Sorbonne Université, 11 Place Marcelin Berthelot, 75231, Paris Cedex 05, France
| | - Anne Lesage
- Centre de RMN à Très Hauts Champs, Université de Lyon (CNRS/ENS Lyon/UCB Lyon 1), 69100, Villeurbanne, France
| | - Florian M Wisser
- Erlangen Center for Interface Research and Catalysis (ECRC), Friedrich-Alexander-Universität Erlangen-Nürnberg, Egerlandstraße 3, 91058, Erlangen, Germany
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12
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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13
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Alegre‐Requena JV, Sowndarya S. V. S, Pérez‐Soto R, Alturaifi TM, Paton RS. AQME: Automated quantum mechanical environments for researchers and educators. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Affiliation(s)
- Juan V. Alegre‐Requena
- Dpto. de Química Inorgánica Instituto de Síntesis Química y Catálisis Homogénea (ISQCH) CSIC‐Universidad de Zaragoza Zaragoza Spain
| | | | - Raúl Pérez‐Soto
- Department of Chemistry Colorado State University Fort Collins Colorado USA
| | - Turki M. Alturaifi
- Department of Chemistry Colorado State University Fort Collins Colorado USA
| | - Robert S. Paton
- Department of Chemistry Colorado State University Fort Collins Colorado USA
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14
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Singh S, Sunoj RB. Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges. Acc Chem Res 2023; 56:402-412. [PMID: 36715248 DOI: 10.1021/acs.accounts.2c00801] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
ConspectusIn the domain of reaction development, one aims to obtain higher efficacies as measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of outcomes from low to high yields/selectivities is expected. While it is not easy to identify all of the factors that might impact the reaction efficiency, complex and nonlinear dependence on the nature of reactants, catalysts, solvents, etc. is quite likely. Developmental stages of newer reactions would typically offer a few hundreds of samples with variations in participating molecules and/or reaction conditions. These "observations" and their "output" can be harnessed as valuable labeled data for developing molecular machine learning (ML) models. Once a robust ML model is built for a specific reaction under development, it can predict the reaction outcome for any new choice of substrates/catalyst in a few seconds/minutes and thus can expedite the identification of promising candidates for experimental validation. Recent years have witnessed impressive applications of ML in the molecular world, most of them aimed at predicting important chemical or biological properties. We believe that an integration of effective ML workflows can be made richly beneficial to reaction discovery.As with any new technology, direct adaptation of ML as used in well-developed domains, such as natural language processing (NLP) and image recognition, is unlikely to succeed in reaction discovery. Some of the challenges stem from ineffective featurization of the molecular space, unavailability of quality data and its distribution, in making the right choice of ML model and its technically robust deployment. It shall be noted that there is no universal ML model suitable for an inherently high-dimensional problem such as chemical reactions. Given these backgrounds, rendering ML tools conducive for reactions is an exciting as well as challenging endeavor at the same time. With the increased availability of efficient ML algorithms, we focused on tapping their potential for small-data reaction discovery (a few hundreds to thousands of samples).In this Account, we describe both feature engineering and feature learning approaches for molecular ML as applied to diverse reactions of high contemporary interest. Among these, catalytic asymmetric hydrogenation of imines/alkenes, β-C(sp3)-H bond functionalization, and relay Heck reaction employed a feature engineering approach using the quantum-chemically derived physical organic descriptors as the molecular features─all designed to predict the enantioselectivity. The selection of molecular features to customize it for a reaction of interest is described, along with emphasizing the chemical insights that could be gathered through the use of such features. Feature learning methods for predicting the yield of Buchwald-Hartwig cross-coupling, deoxyfluorination of alcohols, and enantioselectivity of N,S-acetal formation are found to offer excellent predictions. We propose a transfer learning protocol, wherein an ML model such as a language model is trained on a large number of molecules (105-106) and fine-tuned on a focused library of target task reactions, as an effective alternative for small-data reaction discovery (102-103 reactions). The exploitation of deep neural network latent space as a method for generative tasks to identify useful substrates for a reaction is demonstrated as a promising strategy.
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Affiliation(s)
- Sukriti Singh
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Raghavan B Sunoj
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India.,Centre for Machine Intelligence and Data Science, Indian Institute of Technology Bombay, Mumbai 400076, India
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15
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Khan T, Raza S. Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review. Curr Top Med Chem 2023; 23:1640-1663. [PMID: 36725827 DOI: 10.2174/1568026623666230201144522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/14/2022] [Accepted: 12/19/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body. METHODS This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review. RESULTS Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes. CONCLUSION Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.
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Affiliation(s)
- Tahmeena Khan
- Department of Chemistry, Integral University, Lucknow, 226026, U.P., India
| | - Saman Raza
- Department of Chemistry, Isabella Thoburn College, Lucknow, 226007, U.P., India
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16
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Gallarati S, van Gerwen P, Laplaza R, Vela S, Fabrizio A, Corminboeuf C. OSCAR: an extensive repository of chemically and functionally diverse organocatalysts. Chem Sci 2022; 13:13782-13794. [PMID: 36544722 PMCID: PMC9710326 DOI: 10.1039/d2sc04251g] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022] Open
Abstract
The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance.
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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 LausanneSwitzerland
| | - Puck van Gerwen
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland,National Center for Competence in Research – Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland
| | - Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland,National Center for Competence in Research – Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland
| | - Sergi Vela
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland
| | - Alberto Fabrizio
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland,National Center for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland
| | - Clemence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland,National Center for Competence in Research – Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland,National Center for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne (EPFL)1015 LausanneSwitzerland
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17
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Melnyk N, Iribarren I, Mates‐Torres E, Trujillo C. Theoretical Perspectives in Organocatalysis. Chemistry 2022; 28:e202201570. [PMID: 35792702 PMCID: PMC9804221 DOI: 10.1002/chem.202201570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Indexed: 01/05/2023]
Abstract
It is clear that the field of organocatalysis is continuously expanding during the last decades. With increasing computational capacity and new techniques, computational methods have provided a more economic approach to explore different chemical systems. This review offers a broad yet concise overview of current state-of-the-art studies that have employed novel strategies for catalyst design. The evolution of the all different theoretical approaches most commonly used within organocatalysis is discussed, from the traditional approach, manual-driven, to the most recent one, machine-driven.
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Affiliation(s)
- Nika Melnyk
- School of ChemistryTrinity College DublinCollege GreenDublin2Ireland
| | - Iñigo Iribarren
- School of ChemistryTrinity College DublinCollege GreenDublin2Ireland
| | - Eric Mates‐Torres
- School of ChemistryTrinity College DublinCollege GreenDublin2Ireland
| | - Cristina Trujillo
- School of ChemistryTrinity College DublinCollege GreenDublin2Ireland
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18
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Fey N, Lynam JM. Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1590] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Natalie Fey
- School of Chemistry University of Bristol, Cantock's Close Bristol UK
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19
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Sumiya Y, Harabuchi Y, Nagata Y, Maeda S. Quantum Chemical Calculations to Trace Back Reaction Paths for the Prediction of Reactants. JACS AU 2022; 2:1181-1188. [PMID: 35647604 PMCID: PMC9131471 DOI: 10.1021/jacsau.2c00157] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/31/2022] [Accepted: 04/08/2022] [Indexed: 06/12/2023]
Abstract
The long-due development of a computational method for the ab initio prediction of chemical reactants that provide a target compound has been hampered by the combinatorial explosion that occurs when reactions consist of multiple elementary reaction processes. To address this challenge, we have developed a quantum chemical calculation method that can enumerate the reactant candidates from a given target compound by combining an exhaustive automated reaction path search method with a kinetics method for narrowing down the possibilities. Two conventional name reactions were then assessed by tracing back the reaction paths using this new method to determine whether the known reactants could be identified. Our method is expected to be a powerful tool for the prediction of reactants and the discovery of new reactions.
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Affiliation(s)
- Yosuke Sumiya
- Department
of Chemistry, Faculty of Science, Hokkaido
University, Sapporo, Hokkaido 060-0810, Japan
| | - Yu Harabuchi
- Department
of Chemistry, Faculty 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
| | - Yuuya Nagata
- 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
| | - Satoshi Maeda
- Department
of Chemistry, Faculty 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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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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21
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Lan J, Li X, Yang Y, Zhang X, Chung LW. New Insights and Predictions into Complex Homogeneous Reactions Enabled by Computational Chemistry in Synergy with Experiments: Isotopes and Mechanisms. Acc Chem Res 2022; 55:1109-1123. [PMID: 35385649 DOI: 10.1021/acs.accounts.1c00774] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Homogeneous catalysis and biocatalysis have been widely applied in synthetic, medicinal, and energy chemistry as well as synthetic biology. Driven by developments of new computational chemistry methods and better computer hardware, computational chemistry has become an essentially indispensable mechanistic "instrument" to help understand structures and decipher reaction mechanisms in catalysis. In addition, synergy between computational and experimental chemistry deepens our mechanistic understanding, which further promotes the rational design of new catalysts. In this Account, we summarize new or deeper mechanistic insights (including isotope, dispersion, and dynamical effects) into several complex homogeneous reactions from our systematic computational studies along with subsequent experimental studies by different groups. Apart from uncovering new mechanisms in some reactions, a few computational predictions (such as excited-state heavy-atom tunneling, steric-controlled enantioswitching, and a new geminal addition mechanism) based on our mechanistic insights were further verified by ensuing experiments.The Zimmerman group developed a photoinduced triplet di-π-methane rearrangement to form cyclopropane derivatives. Recently, our computational study predicted the first excited-state heavy-atom (carbon) quantum tunneling in one triplet di-π-methane rearrangement, in which the reaction rates and 12C/13C kinetic isotope effects (KIEs) can be enhanced by quantum tunneling at low temperatures. This unprecedented excited-state heavy-atom tunneling in a photoinduced reaction has recently been verified by an experimental 12C/13C KIE study by the Singleton group. Such combined computational and experimental studies should open up opportunities to discover more rare excited-state heavy-atom tunneling in other photoinduced reactions. In addition, we found unexpectedly large secondary KIE values in the five-coordinate Fe(III)-catalyzed hetero-Diels-Alder pathway, even with substantial C-C bond formation, due to the non-negligible equilibrium isotope effect (EIE) derived from altered metal coordination. Therefore, these KIE values cannot reliably reflect transition-state structures for the five-coordinate metal pathway. Furthermore, our density functional theory (DFT) quasi-classical molecular dynamics (MD) simulations demonstrated that the coordination mode and/or spin state of the iron metal as well as an electric field can affect the dynamics of this reaction (e.g., the dynamically stepwise process, the entrance/exit reaction channels).Moreover, we unveiled a new reaction mechanism to account for the uncommon Ru(II)-catalyzed geminal-addition semihydrogenation and hydroboration of silyl alkynes. Our proposed key gem-Ru(II)-carbene intermediates derived from double migrations on the same alkyne carbon were verified by crossover experiments. Additionally, our DFT MD simulations suggested that the first hydrogen migration transition-state structures may directly and quickly form the key gem-Ru-carbene structures, thereby "bypassing" the second migration step. Furthermore, our extensive study revealed the origin of the enantioselectivity of the Cu(I)-catalyzed 1,3-dipolar cycloaddition of azomethine ylides with β-substituted alkenyl bicyclic heteroarenes enabled by dual coordination of both substrates. Such mechanistic insights promoted our computational predictions of the enantioselectivity reversal for the corresponding monocyclic heteroarene substrates and the regiospecific addition to the less reactive internal C═C bond of one diene substrate. These predictions were proven by our experimental collaborators. Finally, our mechanistic insights into a few other reactions are also presented. Overall, we hope that these interactive computational and experimental studies enrich our mechanistic understanding and aid in reaction development.
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Affiliation(s)
- 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 Catalysis, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Xin Li
- Shenzhen Grubbs Institute, Department of Chemistry, and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Yuhong Yang
- Shenzhen Grubbs Institute, Department of Chemistry, and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Xiaoyong Zhang
- Shenzhen Grubbs Institute, Department of Chemistry, and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Lung Wa Chung
- Shenzhen Grubbs Institute, Department of Chemistry, and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
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22
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Matsuoka W, Harabuchi Y, Maeda S. Virtual Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Wataru Matsuoka
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Yu Harabuchi
- Department of Chemistry, Faculty 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
| | - Satoshi Maeda
- Department of Chemistry, Faculty 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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23
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Wang J, Wang Y, Chen Y. Inverse Design of Materials by Machine Learning. MATERIALS 2022; 15:ma15051811. [PMID: 35269043 PMCID: PMC8911677 DOI: 10.3390/ma15051811] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/13/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023]
Abstract
It is safe to say that every invention that has changed the world has depended on materials. At present, the demand for the development of materials and the invention or design of new materials is becoming more and more urgent since peoples' current production and lifestyle needs must be changed to help mitigate the climate. Structure-property relationships are a vital paradigm in materials science. However, these relationships are often nonlinear, and the pattern is likely to change with length scales and time scales, posing a huge challenge. With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic structure but by using backpropagation to overcome local minimax traps and perform a fast calculation of the gradient information for a target function concerning the design variable to find the optimizations. The methodologies have been applied to various materials including polymers, photonics, inorganic materials, porous materials, 2-D materials, etc. Different types of design problems require different approaches, for which many algorithms and optimization approaches have been demonstrated in different scenarios. In this mini-review, we will not specifically sum up machine learning methodologies, but will provide a more material perspective and summarize some cut-edging studies.
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Affiliation(s)
- Jia Wang
- School of Space and Environment, Beihang University, Beijing 102206, China;
| | - Yingxue Wang
- National Engineering Laboratory for Risk Perception and Prevention, Beijing 100081, China
- Correspondence:
| | - Yanan Chen
- School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China;
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24
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Wolf C, Hassan DS, Kariapper FS, Lynch CC. Accelerated Asymmetric Reaction Screening with Optical Assays. SYNTHESIS-STUTTGART 2022. [DOI: 10.1055/a-1754-2271] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
AbstractAsymmetric reaction development often involves optimization of several mutually dependent parameters that affect the product yield and enantiomeric excess. Widely available high-throughput experimentation equipment and optical sensing assays can drastically streamline comprehensive optimization efforts and speed up the discovery process at reduced cost, workload, and waste production. A variety of chiroptical assays that utilize fluorescence, UV, and circular dichroism measurements to determine reaction yields and ee values are now available, enabling the screening of numerous small-scale reaction samples in parallel with multi-well plate technology. Many of these optical methods considerably shorten work-up protocols typically required for traditional asymmetric reaction analysis and some can be directly applied to crude mixtures thus eliminating cumbersome separation and purification steps altogether.1 Introduction2 Fluorescence Assays3 UV Sensing Methods4 Sensing with Circular Dichroism Probes5 Hybrid Approaches6 Optical Analysis with Intrinsically CD-Active Reaction Products7 Conclusion
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25
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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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26
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Mukai M, Nagao K, Yamaguchi S, Ohmiya H. Molecular Field Analysis Using Computational-Screening Data in Asymmetric N-Heterocyclic Carbene-Copper Catalysis toward Data-driven in silico Catalyst Optimization. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2022. [DOI: 10.1246/bcsj.20210349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Masakiyo Mukai
- Division of Pharmaceutical Sciences, Graduate School of Medical Sciences, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Kazunori Nagao
- Division of Pharmaceutical Sciences, Graduate School of Medical Sciences, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Shigeru Yamaguchi
- RIKEN Center for Sustainable Resource Science, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Hirohisa Ohmiya
- Division of Pharmaceutical Sciences, Graduate School of Medical Sciences, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
- JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
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27
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Chen Q, Guo W, Fu Y. Smart Flow Electrosynthesis and Application of Organodisulfides in Redox Flow Batteries. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2104036. [PMID: 34761570 PMCID: PMC8728815 DOI: 10.1002/advs.202104036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/04/2021] [Indexed: 05/06/2023]
Abstract
Electrochemical techniques have been recognized as an environmentally friendly and sustainable synthetic way to form organodisulfides. However, searching for optimum conditions which suffers from time/material-consuming caused by the uncertainty of reactant consumption has hindered its rapid and large-scale development. Inspired by advanced nonaqueous redox flow batteries (NARFBs) technology, it is proposed a smart flow electrosynthesis (SFE) method of organodisulfides that the voltage curve of NARFBs can be utilized as a precise indicator to reflect the desired information about reactants and distinguish the end point of reaction automatically. This electrochemical method also exhibits certain universality and scalability. Additionally, organodisulfides generated in electrolytes can be used as active species for NARFBs without further purification, and their electrochemical properties are easily adjusted by changing raw materials, which effectively alleviate the waste in complex synthesis steps for optimizing and designing active materials separately. An organodisulfide dervied from isopropyl alcohol and carbon disulfide shows excellent cycling life (1000 cycles) with low capacity fade rate (0.024% per cycle). Taking advantages of the inherent NARFBs, this work not only proves a SFE strategy, but also supplies a green and low-cost molecular engineering scheme for designing electroactive materials for energy storage.
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Affiliation(s)
- Qiliang Chen
- College of ChemistryZhengzhou UniversityZhengzhou450001P. R. China
| | - Wei Guo
- College of ChemistryZhengzhou UniversityZhengzhou450001P. R. China
| | - Yongzhu Fu
- College of ChemistryZhengzhou UniversityZhengzhou450001P. R. China
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28
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Harden I, Neese F, Bistoni G. An induced-fit model for asymmetric organocatalytic reactions: a case study of the activation of olefins via chiral Brønsted acid catalysts. Chem Sci 2022; 13:8848-8859. [PMID: 35975151 PMCID: PMC9350588 DOI: 10.1039/d2sc02274e] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/03/2022] [Indexed: 11/21/2022] Open
Abstract
We elucidate the stereo-controlling factors of the asymmetric intramolecular hydroalkoxylation of terminal olefins catalyzed by bulky Brønsted acids [Science2018, 359 (6383), 1501–1505] using high-level electronic structure methods.
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Affiliation(s)
- Ingolf Harden
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm Platz 1, 45470 Mülheim an der Ruhr, Germany
| | - Frank Neese
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm Platz 1, 45470 Mülheim an der Ruhr, Germany
| | - Giovanni Bistoni
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm Platz 1, 45470 Mülheim an der Ruhr, Germany
- Department of Chemistry, Biology and Biotechnology, University of Perugia Via Elce di Sotto, 8, 06123 Perugia, Italy
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29
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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] [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. The computation of reaction selectivity represents an appealing complementary route to experimental studies and a powerful mean to refine catalyst design strategies.![]()
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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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30
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Hueffel JA, Sperger T, Funes-Ardoiz I, Ward JS, Rissanen K, Schoenebeck F. Accelerated dinuclear palladium catalyst identification through unsupervised machine learning. Science 2021; 374:1134-1140. [PMID: 34822285 DOI: 10.1126/science.abj0999] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Julian A Hueffel
- Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany
| | - Theresa Sperger
- Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany
| | - Ignacio Funes-Ardoiz
- Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany
| | - Jas S Ward
- Department of Chemistry, University of Jyväskylä; P.O. Box 35, 40014 Jyväskylä, Finland
| | - Kari Rissanen
- Department of Chemistry, University of Jyväskylä; P.O. Box 35, 40014 Jyväskylä, Finland
| | - Franziska Schoenebeck
- Institute of Organic Chemistry, RWTH Aachen University; Landoltweg 1, 52074 Aachen, Germany
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31
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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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32
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Lexa KW, Belyk KM, Henle J, Xiang B, Sheridan RP, Denmark SE, Ruck RT, Sherer EC. Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts. Org Process Res Dev 2021. [DOI: 10.1021/acs.oprd.1c00155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Katrina W. Lexa
- Department of Computational and Structural Chemistry, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Kevin M. Belyk
- Department of Process Research & Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Jeremy Henle
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
| | - Bangping Xiang
- Department of Process Research & Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Robert P. Sheridan
- Department of Computational and Structural Chemistry, MRL, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Scott E. Denmark
- Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
| | - Rebecca T. Ruck
- Department of Process Research & Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Edward C. Sherer
- Department of Computational and Structural Chemistry, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States
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33
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Hassan DS, Wolf C. Optical deciphering of multinary chiral compound mixtures through organic reaction based chemometric chirality sensing. Nat Commun 2021; 12:6451. [PMID: 34750404 PMCID: PMC8575934 DOI: 10.1038/s41467-021-26874-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 10/27/2021] [Indexed: 11/24/2022] Open
Abstract
The advances of high-throughput experimentation technology and chemometrics have revolutionized the pace of scientific progress and enabled previously inconceivable discoveries, in particular when used in tandem. Here we show that the combination of chirality sensing based on small-molecule optical probes that bind to amines and amino alcohols via dynamic covalent or click chemistries and powerful chemometric tools that achieve orthogonal data fusion and spectral deconvolution yields a streamlined multi-modal sensing protocol that allows analysis of the absolute configuration, enantiomeric composition and concentration of structurally analogous—and therefore particularly challenging—chiral target compounds without laborious and time-consuming physical separation. The practicality, high accuracy, and speed of this approach are demonstrated with complicated quaternary and octonary mixtures of varying chemical and chiral compositions. The advantages over chiral chromatography and other classical methods include operational simplicity, increased speed, reduced waste production, low cost, and compatibility with multiwell plate technology if high-throughput analysis of hundreds of samples is desired. The stereoselective analysis of mixtures of chiral compounds typically requires time-consuming chromatography. Here, the authors combine reaction based chiroptical sensing and chemometric tools to directly determine the absolute configuration, enantiomeric composition and concentration of convoluted samples without physical separation.
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Affiliation(s)
- Diandra S Hassan
- Department of Chemistry, Georgetown University, Washington, DC, 20057, USA
| | - Christian Wolf
- Department of Chemistry, Georgetown University, Washington, DC, 20057, USA.
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34
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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: 1] [Impact Index Per Article: 0.3] [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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35
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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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36
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Parker PD, Hou X, Dong VM. Reducing Challenges in Organic Synthesis with Stereoselective Hydrogenation and Tandem Catalysis. J Am Chem Soc 2021; 143:6724-6745. [PMID: 33891819 DOI: 10.1021/jacs.1c00750] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Tandem catalysis enables the rapid construction of complex architectures from simple building blocks. This Perspective shares our interest in combining stereoselective hydrogenation with transformations such as isomerization, oxidation, and epimerization to solve diverse challenges. We highlight the use of tandem hydrogenation for preparing complex natural products from simple prochiral building blocks and present tandem catalysis involving transfer hydrogenation and dynamic kinetic resolution. Finally, we underline recent breakthroughs and opportunities for asymmetric hydrogenation.
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Affiliation(s)
- Patrick D Parker
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Xintong Hou
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Vy M Dong
- Department of Chemistry, University of California, Irvine, California 92697, United States
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37
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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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38
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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: 34] [Impact Index Per Article: 11.3] [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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39
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40
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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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41
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Chen Y, Tian B, Cheng Z, Li X, Huang M, Sun Y, Liu S, Cheng X, Li S, Ding M. Electro-Descriptors for the Performance Prediction of Electro-Organic Synthesis. Angew Chem Int Ed Engl 2021; 60:4199-4207. [PMID: 33180375 DOI: 10.1002/anie.202014072] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Indexed: 12/20/2022]
Abstract
Electrochemical organic synthesis has attracted increasing attentions as a sustainable and versatile synthetic platform. Quantitative assessment of the electro-organic reactions, including reaction thermodynamics, electro-kinetics, and coupled chemical processes, can lead to effective analytical tool to guide their future design. Herein, we demonstrate that electrochemical parameters such as onset potential, Tafel slope, and effective voltage can be utilized as electro-descriptors for the evaluation of reaction conditions and prediction of reactivities (yields). An "electro-descriptor-diagram" is generated, where reactive and non-reactive conditions/substances show distinct boundary. Successful predictions of reaction outcomes have been demonstrated using electro-descriptor diagram, or from machine learning algorithms with experimentally-derived electro-descriptors. This method represents a promising tool for data-acquisition, reaction prediction, mechanistic investigation, and high-throughput screening for general organic electro-synthesis.
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Affiliation(s)
- Yuxuan Chen
- Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Bailin Tian
- Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Zheng Cheng
- Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China.,Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Xiaoshan Li
- Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Min Huang
- Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Yuxia Sun
- Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Shuai Liu
- Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Xu Cheng
- Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Shuhua Li
- Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China.,Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Mengning Ding
- Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
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42
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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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44
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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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45
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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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46
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Balcells D, Skjelstad BB. tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes. J Chem Inf Model 2020; 60:6135-6146. [PMID: 33166143 PMCID: PMC7768608 DOI: 10.1021/acs.jcim.0c01041] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Indexed: 12/19/2022]
Abstract
We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal-organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, -1}e. The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm.
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Affiliation(s)
- David Balcells
- Hylleraas
Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
| | - Bastian Bjerkem Skjelstad
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo 001-0021, Japan
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47
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Chen Y, Tian B, Cheng Z, Li X, Huang M, Sun Y, Liu S, Cheng X, Li S, Ding M. Electro‐Descriptors for the Performance Prediction of Electro‐Organic Synthesis. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.202014072] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Yuxuan Chen
- Key Laboratory of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Bailin Tian
- Key Laboratory of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Zheng Cheng
- Key Laboratory of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
- Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Xiaoshan Li
- Key Laboratory of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Min Huang
- Key Laboratory of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Yuxia Sun
- Key Laboratory of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Shuai Liu
- Jiangsu Key Laboratory of Advanced Organic Materials School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Xu Cheng
- Jiangsu Key Laboratory of Advanced Organic Materials School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Shuhua Li
- Key Laboratory of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
- Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
| | - Mengning Ding
- Key Laboratory of Mesoscopic Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China
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48
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Canivet J, Bernoud E, Bonnefoy J, Legrand A, Todorova TK, Quadrelli EA, Mellot-Draznieks C. Synthetic and computational assessment of a chiral metal-organic framework catalyst for predictive asymmetric transformation. Chem Sci 2020; 11:8800-8808. [PMID: 34123133 PMCID: PMC8163446 DOI: 10.1039/d0sc03364b] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Understanding and controlling molecular recognition mechanisms at a chiral solid interface is a continuously addressed challenge in heterogeneous catalysis. Here, the molecular recognition of a chiral peptide-functionalized metal–organic framework (MOF) catalyst towards a pro-chiral substrate is evaluated experimentally and in silico. The MIL-101 metal–organic framework is used as a macroligand for hosting a Noyori-type chiral ruthenium molecular catalyst, namely (benzene)Ru@MIL-101-NH-Gly-Pro. Its catalytic perfomance toward the asymmetric transfer hydrogenation (ATH) of acetophenone into R- and S-phenylethanol are assessed. The excellent match between the experimentally obtained enantiomeric excesses and the computational outcomes provides a robust atomic-level rationale for the observed product selectivities. The unprecedented role of the MOF in confining the molecular Ru-catalyst and in determining the access of the prochiral substrate to the active site is revealed in terms of highly face-specific host–guest interactions. The predicted surface-specific face differentiation of the prochiral substrate is experimentally corroborated since a three-fold increase in enantiomeric excess is obtained with the heterogeneous MOF-based catalyst when compared to its homogeneous molecular counterpart. Understanding and controlling molecular recognition mechanisms at a chiral solid interface has been addressed in metal–organic framework catalysts for the asymmetric transfer hydrogenation reaction.![]()
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Affiliation(s)
- Jérôme Canivet
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON UMR 5256 2 Avenue Albert Einstein 69626 Villeurbanne France
| | - Elise Bernoud
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON UMR 5256 2 Avenue Albert Einstein 69626 Villeurbanne France
| | - Jonathan Bonnefoy
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON UMR 5256 2 Avenue Albert Einstein 69626 Villeurbanne France
| | - Alexandre Legrand
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON UMR 5256 2 Avenue Albert Einstein 69626 Villeurbanne France
| | - Tanya K Todorova
- Laboratoire de Chimie des Processus Biologiques, Collège de France, Sorbonne Université, CNRS UMR 8229, PSL Research University 11 Place Marcelin Berthelot Paris 75231 Cedex 05 France
| | - Elsje Alessandra Quadrelli
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, C2P2 UMR 5265 43 Boulevard du 11 Novembre 1918 69616 Villeurbanne France
| | - Caroline Mellot-Draznieks
- Laboratoire de Chimie des Processus Biologiques, Collège de France, Sorbonne Université, CNRS UMR 8229, PSL Research University 11 Place Marcelin Berthelot Paris 75231 Cedex 05 France
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49
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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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50
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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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