Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression
The random forest regression (RFR) model was introduced to predict the multiple spin state charges of a heme model, which is important for the molecular dynamic simulation of the spin crossover phenomenon. In this work, a multiple spin state structure data set with 39,368 structures of the simplifie...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-03-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fchem.2020.00162/full |
| _version_ | 1819124482535587840 |
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| author | Wei Zhao Qing Li Xian-Hui Huang Li-Hua Bie Jun Gao |
| author_facet | Wei Zhao Qing Li Xian-Hui Huang Li-Hua Bie Jun Gao |
| author_sort | Wei Zhao |
| collection | DOAJ |
| description | The random forest regression (RFR) model was introduced to predict the multiple spin state charges of a heme model, which is important for the molecular dynamic simulation of the spin crossover phenomenon. In this work, a multiple spin state structure data set with 39,368 structures of the simplified heme–oxygen binding model was built from the non-adiabatic dynamic simulation trajectories. The ESP charges of each atom were calculated and used as the real-valued response. The conformational adapted charge model (CAC) of three spin states was constructed by an RFR model using symmetry functions. The results show that our RFR model can effectively predict the on the fly atomic charges with the varying conformations as well as the atomic charge of different spin states in the same conformation, thus achieving the balance of accuracy and efficiency. The average mean absolute error of the predicted charges of each spin state is <0.02 e. The comparison studies on descriptors showed a maximum 0.06 e improvement in prediction of the charge of Fe2+ by using 11 manually selected structural parameters. We hope that this model can not only provide variable parameters for developing the force field of the multi-spin state but also facilitate automation, thus enabling large-scale simulations of atomistic systems. |
| first_indexed | 2024-12-22T07:24:57Z |
| format | Article |
| id | doaj.art-a09ca23318134d4ba4a88fef76b0d39e |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-12-22T07:24:57Z |
| publishDate | 2020-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-a09ca23318134d4ba4a88fef76b0d39e2022-12-21T18:34:11ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462020-03-01810.3389/fchem.2020.00162529852Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest RegressionWei ZhaoQing LiXian-Hui HuangLi-Hua BieJun GaoThe random forest regression (RFR) model was introduced to predict the multiple spin state charges of a heme model, which is important for the molecular dynamic simulation of the spin crossover phenomenon. In this work, a multiple spin state structure data set with 39,368 structures of the simplified heme–oxygen binding model was built from the non-adiabatic dynamic simulation trajectories. The ESP charges of each atom were calculated and used as the real-valued response. The conformational adapted charge model (CAC) of three spin states was constructed by an RFR model using symmetry functions. The results show that our RFR model can effectively predict the on the fly atomic charges with the varying conformations as well as the atomic charge of different spin states in the same conformation, thus achieving the balance of accuracy and efficiency. The average mean absolute error of the predicted charges of each spin state is <0.02 e. The comparison studies on descriptors showed a maximum 0.06 e improvement in prediction of the charge of Fe2+ by using 11 manually selected structural parameters. We hope that this model can not only provide variable parameters for developing the force field of the multi-spin state but also facilitate automation, thus enabling large-scale simulations of atomistic systems.https://www.frontiersin.org/article/10.3389/fchem.2020.00162/fullspin crossoverheme modelforce fieldmachine learningESP charge |
| spellingShingle | Wei Zhao Qing Li Xian-Hui Huang Li-Hua Bie Jun Gao Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression Frontiers in Chemistry spin crossover heme model force field machine learning ESP charge |
| title | Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression |
| title_full | Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression |
| title_fullStr | Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression |
| title_full_unstemmed | Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression |
| title_short | Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression |
| title_sort | toward the prediction of multi spin state charges of a heme model by random forest regression |
| topic | spin crossover heme model force field machine learning ESP charge |
| url | https://www.frontiersin.org/article/10.3389/fchem.2020.00162/full |
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