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Non ionic organic pesticide environmental behaviour: ranking and classification. CLASSIFICATION TREE. MW. 317.69. BAL. 2.53. CLASSIFICATION MODELS RESULTS. nHD. 0.50. 2. 3. 1. 4. ASSIGNED CLASSES. CLUSTER ANALISIS OF PHYSICO-CHEMICAL PROPERTIES. 1: Soluble comp. 2: Medium comp.
Non ionic organic pesticide environmental behaviour: ranking and classification
CLASSIFICATION MODELS RESULTS
CLUSTER ANALISIS OF PHYSICO-CHEMICAL PROPERTIES
1: Soluble comp.
2: Medium comp.
3: Volatile comp.
4: Sorbed comp.
REGRESSION MODELS RESULTS
F. Consolaro and P. Gramatica
QSAR Research Unit, Dept. of Structural and Functional Biology, University of Insubria, Varese, Italy.
E-mail: [email protected]
STRUCTURAL DESCRIPTION OF COMPOUNDS
Molecular descriptors used are: molecular weight, count descriptors (1D-descriptors), obtained from a simple knowledge of the molecular formula, and graph invariant descriptors (2D-descriptors), both topological and information indexes, obtained from the knowledge of the molecular topology and WHIM descriptors2, 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+], by using the software WHIM-3D/QSAR3.
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.
 web download: http://www.disat.unimib.it/chm
Pesticides play an important role in maintaining agricultural productivity, but they may also be the cause of air, water, soil and food contamination with possible adverse effects on human and animal health. Knowledge of pesticide environmental behaviour, i.e. their absorption in the soil, possibility of leaching in groundwater, volatility in the atmosphere, is of primary concern. Data for all the most relevant physico-chemical properties, such as soil adsorption coefficient (Koc)1, n-octanol/water partition coefficient (Kow), water solubility, vapour pressure and Henry’s law constant, are modelled and predicted by the QSPR (Quantitative Structure-Property Relationships) approach.
 P. Gramatica, M. Corradi, V. Consonni Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by different sets of molecular descriptors. Chemosphere, 41/5, (2000), 763-777
QSPR studies were performed on a data set composed by 185 pesticides of the following chemical classes:
6 acetanilides, 40 carbammates, 12 dinitroanilines, 9 organochlorides, 47 organophosphates, 45 phenylureas, 17 triazines and 9 di- and triazoles.
Only for 54 of these chemicals, experimental data of all 5 physico-chemical properties before mentioned are available.
EXPERIMENTAL and PREDICTED DATA (173 compounds)
ONLY 54 EXPERIMENTAL DATA
QSPR PREDICTION OF MISSING DATA and COMPARTMENT-ASSIGNMENT
OF 173 PESTICIDES
Due to the lack of a lot of experimental data for the most relevant physico-chemical properties, the above-mentioned molecular descriptors have been used to obtain OLS regression models with satisfactory predictive power (Q2LOO = 68-83%); the reliability of predicted data has been always checked by the leverage approach. The experimental and predicted data of these properties for a total of 173 pesticides of different chemical classes are then combined in Principal Component Analysis, as shown in the graph below:
RANKING and CLASSIFICATION
The experimental data of the studied properties for 54 pesticides of different chemical classes are combined in Principal Component Analysis and Hierarchical Cluster Analysis to rank the pesticides in 4 a priori classes according to their environmental behaviour (sorbed, soluble, volatile and a mixed class).
The distribution of the 173 chemicals in the PC1-PC2 space shows that the PC1 scores separate sorbed from volatile pesticides, while the PC2 scores separate volatile and not-volatile pesticides.
Thus, regression models by the OLS method are then performed on PC1 and PC2 scores with the aim of predicting these characteristics (volatility, sorption and solubility) for each pesticide starting only from the knowledge of a few theoretical descriptors of molecular structure (count descriptors: nC, nS, nX, nCO, nCIC; unsaturation index UI: hydrophobicity index HYF and WHIM descriptors of global dimension Tm).
Classification by different classification methods (CART, K-NN, LDA and RDA) using only theoretical molecular structure descriptors allows a fast screening of both existing and new pesticides with regard to their behaviour in the environment, based simply on the knowledge of molecular structure.
PC1 MODEL:Q2LOO: 84.9% R2: 85.8% Q2LMO: 84.7%
SDEP: 0.69 SDEC: 0.67
Selected descriptors: nC - nS - nX - nCO
PC2 MODEL: Q2LOO: 78.3% R2: 80.3% Q2LMO: 77.7%
SDEP: 0.52 SDEC: 0.47
Selected descriptors: nCIC - UI - HYF - Tm
MW : molecular weight
BAL : Balaban distance connectivity index
nHD : number of donor atoms for H-bonds
The classification and regression models presented allow an easy “a priori” prediction of the environmental compartment in which existing and new pesticides can be found, starting only from the knowledge of few molecular structure descriptors.