Bio-mimetic self-assembled computationally designed catalysts of Mo and W for hydrogenation of CO<sub>2</sub>/dehydrogenation of HCOOH inspired by the active site of formate dehydrogenase

Shiekh, Bilal Ahmad; Kaur, Deepinder; Kumar, S. Dinesh

doi:10.1039/c9cp03406d

Cited by 8 publications

(9 citation statements)

References 67 publications

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“…The choice of ligands is indicated schematically in blue text and arrows highlight the effect of changing substituents versus changing the axial ligand. Reproduced with permission from ref . Copyright 2019 Royal Society of Chemistry.…”

Section: Statistical Modeling For Transition-metal Chemistrymentioning

confidence: 99%

“…470 By comparing metals, substituents on the alkyne, and ligands in the rest of the complex, they determined 470 CO 2 hydrogenation catalysts to determine the relative magnitude of changing metal oxidation state, functional groups distant from the metal, and axial coordinating ligands (Figure 41). 471 They observed a wide range of feasible H 2 binding energies that could be used to tune the catalysts, with oxidation state playing a pivotal role in comparison to functional group substitution. 471 Similarly, factors governing ligand redox noninnocence are poorly understood, motivating a screen of how altering functional groups on o-benzoquinone ligands affects the behavior of Co(II) complexes.…”

Section: Data Mining Structures For Chemical Trendsmentioning

confidence: 99%

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

et al. 2021

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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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Section: Statistical Modeling For Transition-metal Chemistrymentioning

confidence: 99%

Section: Data Mining Structures For Chemical Trendsmentioning

confidence: 99%

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

et al. 2021

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“…Such an assignment was performed semi-automatically. For instance, a standard name was automatically tagged as “catalysts” if it contained at least one of the following atoms: Zr [ 21 , 22 ], Rh, Ir, Ru, Pt, Pd, Ni, Fe, Co, Os, Cu [ 23 , 24 ], Re [ 24 ], Ag [ 23 , 25 ], Au [ 23 ], W, Mo [ 26 , 27 ], Ti [ 28 ], Mn [ 29 ], In [ 30 ]. We found that such compounds were often recorded in the Reaxys ® database in the “reagents” field.…”

Section: Computational Proceduresmentioning

confidence: 99%

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

Afonina

Mazitov

Nurmukhametova

et al. 2021

IJMS

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The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (~42,000 reactions) from Reaxys® database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues.

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“…[11][12][13][14] Mo/Wcontaining FDHs convert CO2 reversibly to formate (HCOO − ), a chemical fuel and H2 storage compound. [15][16][17][18] Although Mo/W ions frequently appear in nature for the interconversion of CO2 and HCOO − , only a few examples of Mo/W-based homogeneous catalysts [19][20][21][22][23] have been proposed for reduction of CO2 in comparison to costly noble metal catalysts [24][25][26][27] or earth abundant non-toxic metal catalysts. [28][29][30] Therefore, understanding the active site of Mo/W dependent FDH to better isolate the geometric and electronic environment effects is valuable for designing novel bioinspired Mo/W-based molecular catalysts.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Greater Protein Environment on the Electrostatic Potential in Metalloenzyme Active Sites: the Case of Formate Dehydrogenase

Nazemi

Steeves

Kulik

2021

Preprint

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The Mo/W containing metalloenzyme formate dehydrogenase (FDH) is an efficient and selective natural catalyst which reversibly converts CO2 to formate under ambient conditions. A greater understanding of the role of the protein environment in determining the local properties of the FDH active site would enable rational bioinspired catalyst design. In this study, we investigate the impact of the greater protein environment on the electrostatic potential (ESP) of the active site. To model the enzyme environment, we used a combination of long-timescale classical molecular dynamics (MD) and multiscale quantum-mechanical/molecular-mechanical (QM/MM) simulations. We leverage the charge shift analysis method to systematically construct QM regions and analyze the electronic environment of the active site by evaluating the degree of charge transfer between the core active site and the protein environment. The contribution of the terminal chalcogen ligand to the ESP of the metal center is substantial and dependent on the chalcogen identity, with ESPs less negative and similar for Se and S terminal chalcogens than for O regardless of whether the Mo6+ or W6+ metal center is present. Our evaluation reveals that the orientation of the sidechains and ligand conformations will alter the relative trends in the ESP observed for a given metal center or terminal chalcogen, highlighting the importance of sampling dynamic fluctuations in the protein. Overall, our observations suggest that the terminal chalcogen ligand identity plays an important role in the enzymatic activity of FDH.

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Bio-mimetic self-assembled computationally designed catalysts of Mo and W for hydrogenation of CO₂/dehydrogenation of HCOOH inspired by the active site of formate dehydrogenase

Abstract: Bio-inspired Mo and W based catalysts have been designed for catalytic conversion of CO2 to HCOOH or vice versa by stepwise assessment of the chemical environment around the metal center using state-of-the-art density functional theory.

Cited by 8 publications

References 67 publications

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

Influence of the Greater Protein Environment on the Electrostatic Potential in Metalloenzyme Active Sites: the Case of Formate Dehydrogenase

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Bio-mimetic self-assembled computationally designed catalysts of Mo and W for hydrogenation of CO2/dehydrogenation of HCOOH inspired by the active site of formate dehydrogenase

Abstract: Bio-inspired Mo and W based catalysts have been designed for catalytic conversion of CO2 to HCOOH or vice versa by stepwise assessment of the chemical environment around the metal center using state-of-the-art density functional theory.

Cited by 8 publications

References 67 publications

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

Influence of the Greater Protein Environment on the Electrostatic Potential in Metalloenzyme Active Sites: the Case of Formate Dehydrogenase

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Bio-mimetic self-assembled computationally designed catalysts of Mo and W for hydrogenation of CO₂/dehydrogenation of HCOOH inspired by the active site of formate dehydrogenase