To demonstrate the importance of the polarization effects, we firstly generated 100 simple descriptors with RDKit, and evaluated the contributions of each descriptors to the prediction of partition coefficient with two distinct methods, namely, univariate feature selection and mean decrease in impurity (MDI). The full list of the descriptors and details of two feature selection methods could be found in supporting information. (Table ) As shown in Figure (a), top 20 ranked descriptors utilizing the MDI were presented and the importance of single descriptor is reflected by the percentage ratio. We further classified the descriptors into charge related and non-charge related ones. From it, we could draw a conclusion that the partition coefficient are highly related to the charge, as the electrostatic or polarization related descriptors account for 74 % among the top 20 descriptors, and the most important descriptor is PEOE -V SA 6 , which reflects the direct electrostatic interactions hybrid with the surface area. Same picture was also drawn with the help of univariate feature selection. (Figure )