Quantitative structure-property relationship (QSPR) study to predict retention time of polycyclic aromatic hydrocarbons using the random forest and artificial neural network methods
| Autor: | Mahmoud Reza Sohrabi, Fariba Tadayon, Moona Emrarian, Nasser Goudarzi |
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| Rok vydání: | 2020 |
| Předmět: |
Quantitative structure–activity relationship
Artificial neural network 010405 organic chemistry Chemistry Node (networking) Statistical parameter Stepwise regression 010402 general chemistry Condensed Matter Physics 01 natural sciences 0104 chemical sciences Random forest Correlation Physical and Theoretical Chemistry Biological system Retention time |
| Zdroj: | Structural Chemistry. 31:1281-1288 |
| ISSN: | 1572-9001 1040-0400 |
| DOI: | 10.1007/s11224-019-01476-w |
| Popis: | In this study, a quantitative structure-property relationship (QSPR) was used based on powerful methods, including random forest (RF) and feed-forward back-propagation neural network (FFBP-NN). These methods were carried out for modeling and prediction of retention time (RT) values of polycyclic aromatic hydrocarbons (PAHs) compounds. Also, the number of trees (nt) and the number of randomly selected variables to split each node (m) were studied as effective factors in RF method. The best results were obtained at nt = 300 and m = 7. In addition, statistical parameters were investigated, and the correlation coefficients (R2) of the RF (m = 7), stepwise regression artificial neural network (SR-ANN), and RF-ANN models were 0.9954, 0.9920, and 0.9831, respectively. The obtained results show that the RF model has better performance compared to the other models. So, it can be used as a powerful tool for QSPR studies. |
| Databáze: | OpenAIRE |
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