Prediction of Log P of Halogenated Alkanes by Their E_LUMO and Number of Chlorine and Carbon

Hydrophobicity, as measured by Log P, is an important molecular property in assessing toxicity and carcinogenicity of disinfection by-products (DBPs). With increasing public health concerns of DPBs, there are considerable benefits in developing Quantitative Structure Activity Relationship (QSAR) models, capable of accurately predicting Log P, which could be used in health risk assessment of DBPs. In this research, Log P values of 46 halogenated alkanes, as one class of DBP compounds, were used to develop QSAR models for this class. Three molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (E_LUMO)_, Number of Chlorine (N_Cl) and Number of Carbon (N_C) were used in Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated according to the principles set up by the Organization for Economic Co-operation and Development (OECD). Model Applicability Domain (AD) of the developed QSAR models was defined and mechanisms were interpreted. Considering large number of halogenated alkane compounds, the established QSAR models performed very well in terms of goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models have correlation coefficient R² from 81 % to 98 % with the observed Log P. The leverage approach by Williams plot was applied to detect and remove outliers. As a result, the correlation coefficient, R², of the QSAR models increased by approximately 2 % to 13 %, before and after removing the outliers, respectively. The developed QSAR model was statistically validated for its predictive power of Log P by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods.