Computer aided compound identification based on a highly selective topological index

Xiao, Kaixia; Chen, Mengyao; Zhao, Tanfeng; Zhang, Qingyou

doi:10.1016/j.chemolab.2018.05.003

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…A subset of 4837 sp3C−H bonds in the training set and 510 sp3C−H bonds in the test set were retrieved. The equivalent C−H bonds were identified according to the uniqueness of the carbon atom by using a highly discriminating atomic index – the aATID index [18] as one of a series of highly selective topological indices[ 19 , 20 ] previously suggested by one of the authors. After excluding equivalent C−H bonds, we obtained 2039 sp3C−H bonds in the training set and 224 sp3C−H bonds in the test set.…”

Section: Methodsmentioning

confidence: 99%

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

Luan

Zhang

et al. 2022

Molecular Informatics

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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 R2 (0.75) against experimental energies. A prediction scheme was explored that corrects the RF prediction with the average deviation observed for the k nearest neighbours (KNN) in an additional memory of experimental data. The corrected predictions achieved MAE=2.22 kcal/mol for an independent test set of 145 bonds and the corresponding experimental bond energies.

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Section: Methodsmentioning

confidence: 99%