Predicting logP of pesticides using different software.
We compared experimental and calculated logP values using a data set of 235 pesticides and experimental values from four different sources: The Pesticide Manual, Hansch Manual, ANPA and KowWin databases. LogP were calculated with four softwares: HyperChem, Pallas, KowWin and TOPKAT. Crossed comparison of the experimental and calculated values proved useful, especially for pesticides. These are harder to study than simpler organic compounds. Structurally they are complex, heterogeneous and similar to drugs from a chemical point of view. They offer an interesting way to verify the goodness of the different methods. Other studies compared several logP predictors using a single set of experimental values taken as a reference. Here we discuss the utility of the different logP predictors, with reference to experimental data found in different databases. This offers three advantages: (1) it avoids bias due to the assumption that one single data set is correct; (2) a given predictor can be developed on the same data set used for evaluation; (3) it takes account of experimental variability and can compare it with the predictor's variability. In our study Pallas and KowWin gave the best results for prediction, followed by TOPKAT.
D020003:1-Octanol / Q000737:chemistry; D007700:Kinetics; D008956:Models, Chemical; D010575:Pesticides / Q000737:chemistry*; D012986:Software Validation; D014867:Water / Q000737:chemistry
D010575:Pesticides; D014867:Water; D020003:1-Octanol