1. Log Koc = 1.36 + 0.01 MW + 0.28 nNO – 0.19 nHA + 0.33 CIC - 0.27 MAXDP+ 0.05 Ts s = 0.35 F 6, 134 = 119.9 MW: molecular weight nNO: number of NO bonds nHA: number of acceptor atoms for hydrogen bonds CIC: complementary information index MAXDP: maximum positive electrotopological difference Ts: WHIM descriptor of molecular size weighted by electro- topological charge Our representation of a chemical is based on a large number of molecular descriptors, thus an effective variable selection strategy is necessary: GA-VSS (Genetic Algorithm - Variable Subset Selection) was applied to the whole set of descriptors to highlight the most relevant variables in modelling the partition properties of non-ionic pesticides by Ordinary Least Squares regression (OLS). All calculations were performed using the leave-one-out and leave-more-out procedure of cross-validation, maximising the cross-validated explained variance (Q2LOO). Standard Deviation Error in Prediction (SDEP) and Standard Deviation Error in Calculation (SDEC) were also evaluated. In spite of the great variability of the molecular structures of the studied compounds, models with good predictive power were obtained, especially for Koc, Kow and water solubility. The features of the best models obtained for the considered partition properties are reported in Table 1. 5 REGRESSION MODELS Figure 5 The mobility of the pesticides has been studied considering all the physico-chemical properties relevant for the environmental distribution. In figure 2 the Principal Component Analysis of the first two components for the studied physico- chemical properties is reported. At the right we find compounds more soluble and with high mobility (MOBY 1), while at the left there are compounds more sorbed into the soil or biota (MOBY 3); in the middle compounds of medium mobility and high volatility are grouped (MOBY 2). 4 MOBILITY The three MOBY classes defined by PCA have been used as label in the structural analysis of the studied pesticides. In figure 3 it is highlighted the quite homogeneous distribution of the three classes with the exception of the volatiles organochlorides. The hierarchical cluster analysis performed on the physico-chemical properties of the pesticides shows a grouping of the compounds into four clusters, comparable with the classes of MOBY defined by PCA, but with the addition of a separate cluster for the more volatile compounds. MOLECULAR DESCRIPTORS - USE OF QSPRs FOR MOLECULAR DESCRIPTORS - USE OF QSPRs FOR THE PREDICTION OF PARTITION PROPERTIES THE PREDICTION OF PARTITION PROPERTIES Difficulties in obtaining reliable experimental data can be overcome by QSPR studies that furnish a complementary tool for obtaining data about the partition properties of organic pesticides. Quantitative structure-property relationship approaches are based on the assumption that molecular structure is responsible for the physical and chemical properties of a compound, and on the possibility of representing a molecule by numerical descriptors. Thus molecular descriptors represent chemical information, transformed and coded, to deal with chemical, pharmacological, toxicological and environmental problems. In this work many chemical descriptors have been used: the best known are molecular weight, count descriptors (1D-descriptors), obtained from a simple knowledge of the molecular formula, and topological descriptors (2D-descriptors), obtained from the knowledge of the molecular topology. We have also used WHIM descriptors [1], that contain information about the whole 3D- molecular structure in terms of size, symmetry and atom distribution. These indices are calculated from (x,y,z)- coordinates of three-dimensional molecular structures, usually from a minimum energy conformation (obtained by the molecular mechanics method of Allinger, MM+): we obtained 37 non-directional (or global) and 66 directional WHIM descriptors [2]. 2 [1] R.Todeschini and P.Gramatica, 3D-modelling and prediction by WHIM descriptors. Part 5. Theory development and chemical meaning of the WHIM descriptors, Quant.Struct.-Act.Relat., 16 (1997) 113-119. [2] R. Todeschini, WHIM-3D / QSAR - Software for the calculation of the WHIM descriptors. rel. 4.1 for Windows, Talete srl, Milan (Italy) 1996. Download: http://www.disat.unimi.it/chm. A structural representation for 54 non-ionic organic pesticides has been realised by all the reported molecular descriptors (173). A Principal Component Analysis of this representation allows a insight into the molecular features of our data-set. In figure 1 is reported the distribution of the studied pesticides in the PC1-PC2 space (explained variance: 46.3%). It can be noted that the class of organophosphates is of major structural variability, while organochlorides, dinitroanilines, phenylureas and triazines of our data-set have a bigger structural similarity. 3 STRUCTURAL ANALYSIS INTRODUCTION Environmental behaviour of non-ionic organic pesticides – in particular their soil mobility - has become a major issue during the last years. It depends upon a variety of physical, chemical and biological processes, that are usually studied through the partition properties of a compound, such as, among the most relevant, soil sorption coefficient (K oc ), n- octanol/water partition coefficient (K ow ), water solubility, vapour pressure and Henry’s law constant. So, it is necessary to have reliable data of these physico-chemical properties, in order to evaluate the potential dangers for man and nature, connected with the use of non-ionic organic pesticides in agriculture. Unfortunately, for a large number of these compounds, experimental data are unknown or not homogeneous; this fact doesn’t allow an accurate and comparable evaluation of organic pesticides environmental fate. 1 PARTITION PROPERTIES AND SOIL MOBILITY OF NON IONIC ORGANIC PESTICIDES PAOLA GRAMATICA 1, MAURO CORRADI 2 and VIVIANA CONSONNI 2 1 QSAR Research Unit, Dep. of Structural and Functional Biology, University of Insubria, via Dunant 3, I - 21100, Varese (Italy) e-mail: gramati@imiucca.csi.unimi.it web-site: http://andromeda.varbio.unimi.it/  QSAR/ 2 Milano Chemometric Research Group, Dep. of Environmental Sciences, via Emanueli 15, I - 20126, Milano (Italy) CONCLUSIONS The structural analysis of non-ionic organic pesticides by different molecular descriptors combined together with the Principal Component Analysis and Cluster Analysis of several physico-chemical properties allows the ranking of pesticides according to their partition properties and mobility in the soil. The definition of a new mobility index: MOBY, alternative to GUS or LEACH is here proposed. As the mobility of such compounds appears closely related to their structure, the structural representation by various molecular descriptors leads to good QSPR models for predicting data that are not experimentally available. This approach will be useful in the a priori evaluation of the groundwater pollutant pesticides. 6 Figure 2 Figure 3 Figure 4 Figure 1 The best regression model (Fig. 5) points out the importance of molecular size (expressed by MW, CIC and Ts) in determining soil sorption of non-ionic pesticides. Also very important is the presence of electronegative atoms (nHA), these explaining the formation of hydrogen bonds between pesticides and groundwater, thus preventing soil sorption. The importance of electronic properties for soil sorption is highlighted by MAXDP and by the global WHIM descriptor Ts.