Predicting the Aqueous Solubility of PCDD/Fs by using QSPR Method Based on the Molecular Distance-Edge Vector Index

Abstract

The quantitative structure property relationship (QSPR) for the aqueous solubility (S_w) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and S_w was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the developed models. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation is 5.32 and 6.85, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation is 4.47 and 6.79, respectively. It is demonstrated that there is a quantitative relationship between the MDEV index and S_w of PCDD/Fs. Both MLR and ANN are practicable for modeling this relationship. The developed MLR model and ANN model can be used to predict the S_w of PCDD/Fs. Accordingly, the lgS_w of each PCDD/F was predicted by using the developed models.

Key Words:

• aqueous solubility
• molecular distance-edge vector index
• PCDDs
• PCDFs
• QSPR