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The quantitative structure property relationship (QSPR) for gas/particle   partition coefficient, Kp, of polychlorinated biphenyls (PCBs) was   investigated. Molecular distance-edge vector (MDEV) index was used as the   structural descriptor of PCBs. The quantitative relationship between the   MDEV index and log Kp was modeled by 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   ability of the developed models. When the MLR method is used, the root mean   square relative error (RMSRE) of prediction for leave one out cross   validation and external validation is 4.72 and 8.62 respectively. When the   ANN method is employed, the prediction RMSRE of leave one out cross   validation and external validation is 3.87 and 7.47 respectively. It is   demonstrated that the developed models are practicable for predicting the Kp   of PCBs. The MDEV index is shown to be quantitatively related to the Kp of   PCBs

QSPR study on the gas/particle partition coefficient of PCBs by using molecular distance-edge vector index