Dataset Information

Kinetics of N₂ Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study.

ABSTRACT: Diazo compounds are commonly employed as carbene precursors in carbene transfer reactions during a variety of functionalization procedures. Release of N₂ gas from diazo compounds may lead to carbene formation, and the ease of this process is highly dependent on the characteristics of the substituents located in the vicinity of the diazo moiety. A quantum mechanical density functional theory assisted by machine learning was used to investigate the relationship between the chemical features of diazo compounds and the activation energy required for N₂ elimination. Our results suggest that diazo molecules, possessing a higher positive partial charge on the carbene carbon and more negative charge on the terminal nitrogen, encounter a lower energy barrier. A more positive C charge decreases the π-donor ability of the carbene lone pair to the π* orbital of N₂, while the more negative N charge is a result of a weak interaction between N₂ lone pair and vacant p orbital of the carbene. The findings of this study can pave the way for molecular engineering for the purpose of carbene generation, which serves as a crucial intermediate for many chemical transformations in synthetic chemistry.

SUBMITTER: Farshadfar K

PROVIDER: S-EPMC10785077 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Kinetics of N<sub>2</sub> Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study.

Farshadfar Kaveh K Hashemi Arsalan A Khakpour Reza R Laasonen Kari K

ACS omega 20231215 1

Diazo compounds are commonly employed as carbene precursors in carbene transfer reactions during a variety of functionalization procedures. Release of N<sub>2</sub> gas from diazo compounds may lead to carbene formation, and the ease of this process is highly dependent on the characteristics of the substituents located in the vicinity of the diazo moiety. A quantum mechanical density functional theory assisted by machine learning was used to investigate the relationship between the chemical feat ...[more]

PMID: 38222626

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Project description:The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive and time consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free-energy profiles of the ligand unbinding process, focusing on the free-energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (ICs) that form contacts between the protein and the ligand; it then iteratively updates these interactions during a series of biased molecular dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite-temperature string simulations to obtain the free-energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable the further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased "downhill" trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is cyclin-dependent kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for the potential further development of these compounds. We compared the free-energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.

Dataset Information

Kinetics of N₂ Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study.

Publications

Kinetics of N<sub>2</sub> Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study.

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Kinetics of N2 Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study.

Publications

Kinetics of N<sub>2</sub> Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study.

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OmicsDI is part of the ELIXIR infrastructure

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Kinetics of N₂ Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study.