Biomagnification factor (BMF) is an important index of pollutants in food chains but its experimental determination is quite tedious. In this contribution, as the feature descriptors of molecular information, Tchebichef moments (TMs) were calculated from their structural images. Then stepwise regression was employed to establish the prediction model for the logBMF of organochlorine pollutants. The correlation coefficient with leave-one-out cross-validation (Rcv) was 0.9570; the correlation coefficient of prediction (Rp) and root mean square error (RMSEp) for external independent test set reached 0.9594 and 0.2129, respectively. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) and the reported augmented multivariate image analysis applied to QSPR (aug-MIA-QSPR), the proposed approach is more simple, accurate and reliable. This study not only obtained the model with better stability and predictive ability for the BMF of organochlorine pollutants, but also provided another effective approach to QSPR research.
Although biomagnification factor (BMF) is an important index of pollutants in food chains, its experimental determination is quite tedious. In this contribution, as the feature information, Tchebichef moments (TMs) were calculated directly from the molecular structural images, and then stepwise regression was employed to establish the prediction model of the logBMF. The proposed approach was applied to the logBMF prediction of organochlorine pollutants, and the correlation coefficient with leave-one-out cross-validation (Rcv) of the obtained model was 0.96, and the root mean square error (RMSEp) for the external independent test set was 0.21. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) as well as the reported method, the proposed approach was more simple, accurate and reliable. This study not only obtained the satisfactory prediction model for organochlorine pollutants, but also provided another effective approach to QSPR research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.