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

Formate Dehydrogenase Mimics as Catalysts for Carbon Dioxide Reduction

Formate dehydrogenases (FDH) reversibly catalyze the interconversion of CO₂ to formate. They belong to the family of molybdenum and tungsten-dependent oxidoreductases. For several decades, scientists have been synthesizing structural and functional model complexes inspired by these enzymes. These studies not only allow for finding certain efficient catalysts but also in some cases to better understand the functioning of the enzymes. However, FDH models for catalytic CO₂ reduction are less studied compared to the oxygen atom transfer (OAT) reaction. Herein, we present recent results of structural and functional models of FDH.

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

The Mo/W-containing metalloenzyme formate dehydrogenase (FDH) is an efficient and selective natural catalyst that reversibly converts CO₂ to formate under ambient conditions. 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 classical molecular dynamics and multiscale quantum-mechanical (QM)/molecular-mechanical (MM) simulations. We leverage charge shift analysis 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 similar, less negative ESPs for Se and S terminal chalcogens in comparison to O regardless of whether the metal is Mo or W. The orientation of the side chains and conformations of the cofactor also affect the ESP, 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, suggesting opportunities for a rational bioinspired catalyst design.

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

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.

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

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.

Biomimetic heterobimetallic architecture of Ni(ii) and Fe(ii) for CO2 hydrogenation in aqueous media. A DFT study

In this work, density functional theory has been employed to design a heterobimetallic catalyst of Ni(ii) and Fe(ii) for the effective CO₂ hydrogenation to HCOOH. Based on computational results, our newly designed catalyst is found to be effective for such conversion reactions with free energy as low as 14.13 kcal mol⁻¹ for the rate determining step. Such a low value of free energy indicates that the NiFe heterobimetallic catalyst can prove to be very efficient for the above said conversion. Moreover, the effects of ligand substitutions at the active metal center and the effects due to various spin states are also explored, and can serve as a great tool for the rational design of NiFe catalyst for CO₂ hydrogenation.

The hydrogenation of CO₂ by our newly designed [NiFe] heterobimetallic catalyst inspired by the active site of [NiFe] hydrogenase.