ArticleMolecular informatics2023
Machine Learning to Predict Homolytic Dissociation Energies of C-H Bonds: Calibration of DFT-based Models with Experimental Data.
Article in Molecular informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Exploring Molecular Heteroencoders with Latent Space Arithmetic: Atomic Descriptors and Molecular Operators.Molecules (Basel, Switzerland) · 2024Article
- Structural Characterization of the Staphylococcus aureus Targeting Lectin Peptides from Garlic (Allium sativum L) by Liquid Nitrogen Grinding Coupled with the Proteomic and Antimicrobial Mechanism Analysis.Probiotics and antimicrobial proteins · 2024Article
- Machine Learning to Predict Homolytic Dissociation Energies of C-H Bonds: Calibration of DFT-based Models with Experimental Data.Molecular informatics · 2023Article
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4 authors.
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Abstract
Random Forest (RF) QSPR models were developed with a data set of homolytic bond dissociation energies (BDE) previously calculated by B3LYP/6-311++G(d,p)//DFTB for 2263 sp3C-H covalent bonds. The best set of attributes consisted in 114 descriptors of the carbon atom (counts of atom types in 5 spheres around the kernel atom and ring descriptors). The optimized model predicted the DFT-calculated BDE of an independent test set of 224 bonds with MAE=2.86 kcal/mol. A new data set of 409 bonds from the iBonD database (http://ibond.nankai.edu.cn) was predicted by the RF with a modest MAE (5.36 kcal/mol) but a relatively high R
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