Li, WanliLuan, YueZhang, QingyouAires-de-Sousa, Joao2023-03-202023-03-202023-011868-1743PURE: 56422303PURE UUID: 0ed53559-132d-444a-9197-861d80922f3bScopus: 85140059284WOS: 000870061600001PubMed: 36167940http://hdl.handle.net/10362/150947JAS thanks David Ponting and co‐workers at Lhasa Limited for useful suggestions and discussions. This work was also supported by the National Natural Science Foundation of China [Grant number 21875061, 21975066] and the program for Science & Technology Innovation Team in Universities of Henan Province [Grant number 19IRTSTHN029]. Publisher Copyright: © 2022 The Authors. Molecular Informatics published by Wiley-VCH GmbH.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.81137999engbond energydensity functional calculationslearning transfermachine learningquantitative structure-property relationshipStructural BiologyMolecular MedicineDrug DiscoveryComputer Science ApplicationsOrganic Chemistryjournal article10.1002/minf.202200193https://www.scopus.com/pages/publications/85140059284