Modelling the water–plant cuticular polymer matrix membrane partitioning of diverse chemicals in multiple plant species using the support vector machine-based QSAR approach

Abstract

In this study, a support vector machine (SVM) based multi-species QSAR (quantitative structure–activity relationship) model was developed for predicting the water–plant cuticular polymer matrix membrane (MX) partition coefficient, K_MXw of diverse chemicals using two simple molecular descriptors derived from the chemical structures and following the OECD guidelines. Accordingly, the Lycopersicon esculentum Mill. data were used to construct the QSAR model that was externally validated using three other plant species data. The diversity in chemical structures and end-points were verified using the Tanimoto similarity index and Kruskal–Wallis statistics. The predictive power of the developed QSAR model was tested through rigorous validation, deriving a wide series of statistical checks. The MLOGP was the most influential descriptor identified by the model. The model yielded a correlation (r²) of 0.966 and 0.965 in the training and test data arrays. The developed QSAR model also performed well in another three plant species (r² > 0.955). The results suggest the appropriateness of the developed model to reliably predict the plant chemical interactions in multiple plant species and it can be a useful tool in screening the new chemical for environmental risk assessment.

Keywords:

• Plant chemical interaction
• structural diversity
• multi-species QSARs
• molecular descriptors
• Lycopersicon esculentum Mill

Acknowledgements

The authors thank the Director, CSIR-National Botanical Research Institute, Lucknow (India) for his keen interest in this work and providing all necessary facilities. Suggestions and help provided by Dr Kunwar P. Singh (Ex. Chief Scientist, CSIR-IITR, Lucknow) are thankfully acknowledged. Suggestions and help in predictive modelling provided by Dr Nikita Basant (ETRC, Lucknow) are thankfully acknowledged.