期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 57, 期 1, 页码 11-21出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.6b00340
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资金
- Fundacao para a Ciencia e a Tecnologia (FCT/MEC) Portugal [PEst-OE/QUI/UI0612/2013, SFRH/BPD/63192/2009, SFRH/BPD/108237/2015]
- Associated Laboratory for Sustainable Chemistry-Clean Processes and Technologies-LAQV - national funds from FCT/MEC [UID/QUI/50006/2013]
- ERDF [POCI-01-0145-FEDER-007265]
- National Natural Science Foundation of China [20875022]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China [20091001]
Machine learning algorithms were explored for the fast estimation of HOMO and LUMO orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively based on connectivity. The whole project involved the retrieval and generation of molecular structures, quantum chemical calculations for a database with >111 000 structures, development of new molecular descriptors, and training/validation of machine learning models. Several machine learning algorithms were screened, and an applicability domain was defined based oil Euclidean distances to the training set. Random forest models predicted an external test set of 9989 compounds achieving mean absolute error (MAE) up to 0.15 and 0.16 eV for the HOMO and LUMO orbitals, respectively. The impact of the quantum chemical calculation protocol was assessed with a subset of compounds. Inclusion of the orbital energy calculated by PM7 as an additional descriptor significantly improved the quality of estimations (reducing the MAE in >30%).
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