Machine Learning to Predict Homolytic Dissociation Energies of C−H Bonds: Calibration of DFT‐based Models with Experimental Data-Reference-Cited by-同舟云学术

Machine Learning to Predict Homolytic Dissociation Energies of C−H Bonds: Calibration of DFT‐based Models with Experimental Data

Published:2022-10-19 Issue:1 Volume:42 Page:2200193
ISSN:1868-1743
Container-title:Molecular Informatics
language:en
Short-container-title:Molecular Informatics

Author:

Li Wanli¹,Luan Yue¹,Zhang Qingyou¹,Aires‐de‐Sousa Joao²

Affiliation:

1. Henan Engineering Research Center of Industrial Circulating Water Treatment Henan Joint International Research Laboratory of Environmental Pollution Control Materials Henan University Kaifeng 475004 P.R. China

2. LAQV and REQUIMTE Chemistry Department NOVA School of Science and Technology Universidade Nova de Lisboa 2829-516 Caparica Portugal

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,Molecular Medicine,Structural Biology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/minf.202200193

Reference25 articles.

1. The impact of carbon–hydrogen bond dissociation energies on the prediction of the cytochrome P450 mediated major metabolic site of drug-like compounds

2. Assessment of the metabolic stability of the methyl groups in heterocyclic compounds using C?H bond dissociation energies: Effects of diverse aromatic groups on the stability of methyl radicals

3. Predicting Drug Substances Autoxidation

4. Antioxidant Motifs in Flavonoids: O–H versus C–H Bond Dissociation

5. Computationally Efficient Methodology to Calculate C−H and C−X (X = F, Cl, and Br) Bond Dissociation Energies in Haloalkanes

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