1
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Stuyver T. TS-tools: Rapid and automated localization of transition states based on a textual reaction SMILES input. J Comput Chem 2024. [PMID: 38850166 DOI: 10.1002/jcc.27374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/08/2024] [Accepted: 03/20/2024] [Indexed: 06/10/2024]
Abstract
Here, TS-tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono- and bimolecular reactions, TS-tools reaches an excellent success rate of 95% already at xTB level of theory. For tri- and multimolecular reaction pathways - which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent- and autocatalysis and enzymatic reactivity - TS-tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation-induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far.
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Affiliation(s)
- Thijs Stuyver
- Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, Paris, France
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2
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Steiner M, Reiher M. A human-machine interface for automatic exploration of chemical reaction networks. Nat Commun 2024; 15:3680. [PMID: 38693117 PMCID: PMC11063077 DOI: 10.1038/s41467-024-47997-9] [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: 08/31/2023] [Accepted: 04/15/2024] [Indexed: 05/03/2024] Open
Abstract
Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.
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Affiliation(s)
- Miguel Steiner
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
- ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland
| | - Markus Reiher
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
- ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
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3
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Rasmussen MH, Seumer J, Jensen JH. Toward De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates for Alcohol-Mediated Morita-Baylis-Hillman Reactions. Angew Chem Int Ed Engl 2023; 62:e202310580. [PMID: 37830522 DOI: 10.1002/anie.202310580] [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: 07/25/2023] [Revised: 09/15/2023] [Accepted: 10/13/2023] [Indexed: 10/14/2023]
Abstract
Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than DABCO, commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate-determining step. Thus, truly de novo catalyst discovery must include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The method relies on an automated reaction discovery workflow using meta-dynamics calculations. Combining this method for catalyst candidate discovery with our GA-based catalyst optimization method results in an algorithm for truly de novo catalyst discovery.
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Affiliation(s)
- Maria H Rasmussen
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100, Copenhagen, Denmark
| | - Julius Seumer
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100, Copenhagen, Denmark
| | - Jan H Jensen
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100, Copenhagen, Denmark
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4
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Zuzak R, Quiroga S, Engelund M, Pérez D, Peña D, Godlewski S, Melle-Franco M. Sequential On-Surface Cyclodehydrogenation in a Nonplanar Nanographene. J Phys Chem Lett 2023; 14:10442-10449. [PMID: 37962022 DOI: 10.1021/acs.jpclett.3c02710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
On-surface synthesis has emerged as an attractive method for the atomically precise synthesis of new molecular nanostructures, being complementary to the widespread approach based on solution chemistry. It has been particularly successful in the synthesis of graphene nanoribbons and nanographenes. In both cases, the target compound is often generated through cyclodehydrogenation reactions, leading to planarization and the formation of hexagonal rings. To improve the flexibility and tunability of molecular units, however, the incorporation of other, nonbenzenoid, subunits is highly desirable. In this letter, we thoroughly analyze sequential cyclodehydrogenation reactions with a custom-designed molecular precursor. We demonstrate the step-by-step formation of hexagonal and pentagonal rings from the nonplanar precursor within fjord and cove regions, respectively. Computer models comprehensively support the experimental observations, revealing that both reactions imply an initial hydrogen abstraction and a final [1,2] hydrogen shift, but the formation of a pentagonal ring proceeds through a radical mechanism.
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Affiliation(s)
- Rafal Zuzak
- Centre for Nanometer-Scale Science and Advanced Materials, NANOSAM, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, PL 30-348 Kraków, Poland
| | - Sabela Quiroga
- Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Departamento de Química Orgánica, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Mads Engelund
- Espeem S.A.R.L., L-4365 Esch-sur-Alzette, Luxembourg
| | - Dolores Pérez
- Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Departamento de Química Orgánica, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Diego Peña
- Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Departamento de Química Orgánica, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Szymon Godlewski
- Centre for Nanometer-Scale Science and Advanced Materials, NANOSAM, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, PL 30-348 Kraków, Poland
| | - Manuel Melle-Franco
- CICECO─Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
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5
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Chen Y, Ou Y, Zheng P, Huang Y, Ge F, Dral PO. Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights. J Chem Phys 2023; 158:074103. [PMID: 36813722 DOI: 10.1063/5.0137101] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method.
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Affiliation(s)
- Yuxinxin Chen
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yanchi Ou
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Peikun Zheng
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yaohuang Huang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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6
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Lavigne C, Gomes G, Pollice R, Aspuru-Guzik A. Guided discovery of chemical reaction pathways with imposed activation. Chem Sci 2022; 13:13857-13871. [PMID: 36544742 PMCID: PMC9710306 DOI: 10.1039/d2sc05135d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022] Open
Abstract
Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reaction pathways can be efficiently predicted in a guided manner using chemical activation imposed by geometrical constraints of specific reactive modes, which we term imposed activation (IACTA). Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product, a water-mediated Michael addition, and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of the products or the associated mechanisms. We believe that IACTA will be a transformational tool to screen for chemical reactivity and to study both by-product formation and decomposition pathways in a guided way.
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Affiliation(s)
- Cyrille Lavigne
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada
| | - Gabe Gomes
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada
| | - Robert Pollice
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto214 College St.TorontoOntarioM5T 3A1Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto80 St George StTorontoOntarioM5S 3H6Canada,Department of Chemical Engineering & Applied Chemistry, University of Toronto200 College St.OntarioM5S 3E5Canada,Department of Materials Science & Engineering, University of Toronto184 College St.OntarioM5S 3E4Canada,Vector Institute for Artificial Intelligence661 University Ave Suite 710TorontoOntarioM5G 1M1Canada,Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR)661 University AveTorontoOntarioM5GCanada
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7
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Bursch M, Mewes J, Hansen A, Grimme S. Best-Practice DFT Protocols for Basic Molecular Computational Chemistry. Angew Chem Int Ed Engl 2022; 61:e202205735. [PMID: 36103607 PMCID: PMC9826355 DOI: 10.1002/anie.202205735] [Citation(s) in RCA: 128] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Indexed: 01/11/2023]
Abstract
Nowadays, many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share of these quantum-chemical calculations applies density functional theory (DFT) evaluated in atomic-orbital basis sets. This work provides best-practice guidance on the numerous methodological and technical aspects of DFT calculations in three parts: Firstly, we set the stage and introduce a step-by-step decision tree to choose a computational protocol that models the experiment as closely as possible. Secondly, we present a recommendation matrix to guide the choice of functional and basis set depending on the task at hand. A particular focus is on achieving an optimal balance between accuracy, robustness, and efficiency through multi-level approaches. Finally, we discuss selected representative examples to illustrate the recommended protocols and the effect of methodological choices.
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Affiliation(s)
- Markus Bursch
- Max-Planck-Institut für KohlenforschungKaiser-Wilhelm-Platz 145470Mülheim an der RuhrGermany
| | - Jan‐Michael Mewes
- Mulliken Center for Theoretical ChemistryInstitut für Physikalische und Theoretische ChemieUniversität BonnBeringstraße 453115BonnGermany
| | - Andreas Hansen
- Mulliken Center for Theoretical ChemistryInstitut für Physikalische und Theoretische ChemieUniversität BonnBeringstraße 453115BonnGermany
| | - Stefan Grimme
- Mulliken Center for Theoretical ChemistryInstitut für Physikalische und Theoretische ChemieUniversität BonnBeringstraße 453115BonnGermany
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8
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Iwanek W. Theoretical calculations of formation and reactivity of o-quinomethide derivatives of resorcin[4]arene with reference to empirical data. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220541. [PMID: 36249340 PMCID: PMC9554518 DOI: 10.1098/rsos.220541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
This paper describes theoretical reaction pathways of alkoxybenzyl derivatives of resorcin[4]arene leading to the formation of o-quinomethide derivatives of resorcin[4]arene (o-QMR[4]A). For each case, the activation energies for the formation of one o-QMR[4]A unit and the activation energies for the backward reaction were calculated. Based on the calculated reaction pathways, the reaction mechanism of o-QMR[4]A formation was proposed. Using the example of o-QMR[4]A generated from a methoxy derivative of resorcin[4]arene, the activation energies with selected nucleophiles were calculated and the reaction mechanisms discussed. Reaction path calculations were performed using the nudged elastic band method and semiempirical extended tight-binding method (GFN2-xTB). Using hydroxybenzyl derivatives of resorcin[4]arene as an example, a comparison of calculated activation energies by selected density-functional theory methods with GFN2-xTB and B97-3c geometries was performed. B97-3c and wB97XD methods were used to calculate the energies of the reactants (R), transition states (TS) and products (P) of the analysed reactions. Theoretical reaction mechanisms were discussed with respect to the orbital-weighted Fukui dual descriptor (Δfw ) and experimental data.
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Affiliation(s)
- Waldemar Iwanek
- Faculty of Chemical Technology and Engineering, Bydgoszcz University of Science and Technology, Seminaryjna 3, 85-326 Bydgoszcz, Poland
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9
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Bursch M, Mewes J, Hansen A, Grimme S. Best‐Practice DFT Protocols for Basic Molecular Computational Chemistry**. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202205735] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Markus Bursch
- Max-Planck-Institut für Kohlenforschung Kaiser-Wilhelm-Platz 1 45470 Mülheim an der Ruhr Germany
| | - Jan‐Michael Mewes
- Mulliken Center for Theoretical Chemistry Institut für Physikalische und Theoretische Chemie Universität Bonn Beringstraße 4 53115 Bonn Germany
| | - Andreas Hansen
- Mulliken Center for Theoretical Chemistry Institut für Physikalische und Theoretische Chemie Universität Bonn Beringstraße 4 53115 Bonn Germany
| | - Stefan Grimme
- Mulliken Center for Theoretical Chemistry Institut für Physikalische und Theoretische Chemie Universität Bonn Beringstraße 4 53115 Bonn Germany
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10
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Unsleber JP, Grimmel SA, Reiher M. Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks. J Chem Theory Comput 2022; 18:5393-5409. [PMID: 35926118 DOI: 10.1021/acs.jctc.2c00193] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Fueled by advances in hardware and algorithm design, large-scale automated explorations of chemical reaction space have become possible. Here, we present our approach to an open-source, extensible framework for explorations of chemical reaction mechanisms based on the first-principles of quantum mechanics. It is intended to facilitate reaction network explorations for diverse chemical problems with a wide range of goals such as mechanism elucidation, reaction path optimization, retrosynthetic path validation, reagent design, and microkinetic modeling. The stringent first-principles basis of all algorithms in our framework is key for the general applicability that avoids any restrictions to specific chemical systems. Such an agile framework requires multiple specialized software components of which we present three modules in this work. The key module, Chemoton, drives the exploration of reaction networks. For the exploration itself, we introduce two new algorithms for elementary-step searches that are based on Newton trajectories. The performance of these algorithms is assessed for a variety of reactions characterized by a broad chemical diversity in terms of bonding patterns and chemical elements. Chemoton successfully recovers the vast majority of these. We provide the resulting data, including large numbers of reactions that were not included in our reference set, to be used as a starting point for further explorations and for future reference.
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Affiliation(s)
- Jan P Unsleber
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Stephanie A Grimmel
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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11
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Zeng T, Hess BA, Zhang F, Wu R. Bio-inspired chemical space exploration of terpenoids. Brief Bioinform 2022; 23:6586263. [PMID: 35576010 DOI: 10.1093/bib/bbac197] [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: 04/01/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 11/12/2022] Open
Abstract
Many computational methods are devoted to rapidly generating pseudo-natural products to expand the open-ended border of chemical spaces for natural products. However, the accessibility and chemical interpretation were often ignored or underestimated in conventional library/fragment-based or rule-based strategies, thus hampering experimental synthesis. Herein, a bio-inspired strategy (named TeroGen) is developed to mimic the two key biosynthetic stages (cyclization and decoration) of terpenoid natural products, by utilizing physically based simulations and deep learning models, respectively. The precision and efficiency are validated for different categories of terpenoids, and in practice, more than 30 000 sesterterpenoids (10 times as many as the known sesterterpenoids) are predicted to be linked in a reaction network, and their synthetic accessibility and chemical interpretation are estimated by thermodynamics and kinetics. Since it could not only greatly expand the chemical space of terpenoids but also numerate plausible biosynthetic routes, TeroGen is promising for accelerating heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids and derivatives.
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Affiliation(s)
- Tao Zeng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P.R. China
| | | | - Fan Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P.R. China
| | - Ruibo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P.R. China
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12
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Barrionuevo MVF, Andrés J, San-Miguel MA. A Theoretical Study on the Structural, Electronic, and Magnetic Properties of Bimetallic Pt13−nNin (N = 0, 3, 6, 9, 13) Nanoclusters to Unveil the Catalytic Mechanisms for the Water-Gas Shift Reaction. Front Chem 2022; 10:852196. [PMID: 35518715 PMCID: PMC9063635 DOI: 10.3389/fchem.2022.852196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
In this work, first-principles calculations by using density functional theory at the GFN-xTB level, are performed to investigate the relative stability and structural, electronic, and magnetic properties of bimetallic Pt13−nNin (n = 0, 3, 6, 9, 13) nanoclusters by using corrected Hammer and Nørskov model. In addition, by employing the reaction path and the energetic span models, the energy profile and the turnover frequency are calculated to disclose the corresponding reaction mechanism of the water-gas shift reaction catalyzed by these nanoclusters. Our findings render that Ni causes an overall shrinking of the nanocluster’s size and misalignment of the spin channels, increasing the magnetic nature of the nanoclusters. Pt7Ni6 nanocluster is the most stable as a result of the better coupling between the Pt and Ni d-states. Pt4Ni9 maintains its structure over the reaction cycle, with a larger turnover frequency value than Pt7Ni6. On the other hand, despite Pt10Ni3 presenting the highest value of turnover frequency, it suffers a strong structural deformation over the completion of a reaction cycle, indicating that the catalytic activity can be altered.
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Affiliation(s)
- Manoel Victor Frutuoso Barrionuevo
- UNICAMP Materials Simulation Lab, Institute of Chemistry, Department of Physical-Chemistry, University of Campinas, Campinas, Brazil
- Química Teórica y Computacional, Department de Química Física i Analítica, Universitat Jaume I, Castellón de la Plana, Spain
| | - Juan Andrés
- Química Teórica y Computacional, Department de Química Física i Analítica, Universitat Jaume I, Castellón de la Plana, Spain
- *Correspondence: Juan Andrés, ; Miguel Angel San-Miguel,
| | - Miguel Angel San-Miguel
- UNICAMP Materials Simulation Lab, Institute of Chemistry, Department of Physical-Chemistry, University of Campinas, Campinas, Brazil
- *Correspondence: Juan Andrés, ; Miguel Angel San-Miguel,
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13
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Rasmussen MH, Jensen JH. Fast and automated identification of reactions with low barriers using meta-MD simulations. PEERJ PHYSICAL CHEMISTRY 2022. [DOI: 10.7717/peerj-pchem.22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions on uni- and bimolecular reactions extracted from the barrier dataset developed by Grambow, Pattanaik & Green (2020). For unimolecular reactions the meta-MD simulations identify 25 of the 26 products found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional four reactions due to an overestimation of the reaction energies or estimated barrier heights relative to DFT. In addition, our approach identifies 36 reactions not found by Grambow, Pattanaik & Green (2020), 10 of which are <30 kcal/mol. For bimolecular reactions the meta-MD simulations identify 19 of the 20 reactions found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional reaction. In addition, we find 34 new low-barrier reactions. For bimolecular reactions we found that it is necessary to “encourage” the reactants to go to previously undiscovered products, by including products found by other MD simulations when computing the biasing potential as well as decreasing the size of the molecular cavity in which the MD occurs, until a reaction is observed. We also show that our methodology can find the correct products for two reactions that are more representative of those encountered in synthetic organic chemistry. The meta-MD hyperparameters used in this study thus appear to be generally applicable to finding low-barrier reactions.
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14
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Farrar EHE, Grayson MN. Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction. Chem Sci 2022; 13:7594-7603. [PMID: 35872815 PMCID: PMC9242013 DOI: 10.1039/d2sc02925a] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/08/2022] [Indexed: 11/21/2022] Open
Abstract
A synergistic approach that combines machine learning with semi-empirical methods enables the fast and accurate prediction of DFT-quality reaction barriers, with mechanistic insights available from semi-empirical transition state geometries.
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Affiliation(s)
- Elliot H. E. Farrar
- Department of Chemistry, University of Bath, Claverton Down, Bath, BA2 7AY, UK
| | - Matthew N. Grayson
- Department of Chemistry, University of Bath, Claverton Down, Bath, BA2 7AY, UK
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15
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Ertl P, Gerebtzoff G, Lewis RA, Muenkler H, Schneider N, Sirockin F, Stiefl N, Tosco P. Chemical reactivity prediction: current methods and different application areas. Mol Inform 2021; 41:e2100277. [PMID: 34964302 DOI: 10.1002/minf.202100277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/28/2021] [Indexed: 11/10/2022]
Abstract
The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided. Through machine learning, predictive models can be built out of descriptors derived from quantum mechanics and cheminformatics, even in the absence of experimental data to train on. In this article, current progress on computational reactivity prediction is reviewed: applications to problems in drug design, such as modelling of metabolism and covalent inhibition, are highlighted and unmet challenges are posed.
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Affiliation(s)
| | | | - Richard A Lewis
- Computer-Aided Drug Design, Eli Lilly and Company Limited, Windlesham, SWITZERLAND
| | - Hagen Muenkler
- Novartis Institutes for BioMedical Research Inc, SWITZERLAND
| | | | | | | | - Paolo Tosco
- Novartis Institutes for BioMedical Research Inc, SWITZERLAND
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