Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches. (April 2016)

• Record Type: Journal Article
• Authors: Basant, Nikita; Gupta, Shikha; Singh, Kunwar P.
• Is Part Of: Computational biology and chemistry. Volume 61(2016)
• Page Start: 178
• Page End: 196
• Publication Date: 2016-04
• Subjects: Human intestinal absorption -- Ensemble learning -- Structure–activity relationship -- Diverse chemicals -- Qualitative and quantitative models; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.01.005
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Qualitative/quantitative QSARs developed for predicting HIA of chemicals. Structural diversity and nonlinearity in data tested using TSI and BDS statistics. QSARs validated through OECD recommended stringent parameters. Proposed QSARs precisely predicted HIA of diverse chemicals. Proposed QSARs can be useful tools in screening new drug molecules. Abstract: Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure–activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock–Dechert–Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of >99%, while the quantitative SAR models yielded correlation ( R 2 ) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process.