This thesis focuses on the development of quantitative structure-activity relationship (QSPR) models for physicochemical properties, e.g., vapor pressure and partitioning coefficients. Such models can be used to estimate environmental distribution and transformation of the pollutants or to characterize solvents properties. Here, chemoinformatics was used as an efficient tool for modeling to produce safe chemicals based on green chemistry principles.

Experimental determinations are only available for a limited number of the chemicals; however, theoretical molecular descriptors can be used for modeling of all organic compounds. In this thesis, we developed and validated a global and local QSPR model for vapor pressure of liquid and subcooled liquid organic compounds, in which perfluorinated compounds (PFCs) as outliers appeared in the model due to their molecular properties. Subsequently, after the update of the previous model, the vapor pressure of perfluorinated compounds (PFCs) for which no reliable experimental data are available was successfully predicted. At the same time, we used partitioning between n-octanol/water (K_ow) and water solubility (S_w) to investigate the similarities and differences between linear solvation energy relationship (LSER) and partial least square projection to latent structures (PLS) models. Further, we developed QSPR model for prediction of melting points and boiling points of PFCs using multiple linear regression (MLR), PLS and associative neural networks (ASNN) approaches, meanwhile, the applicability domain of PFCs was also investigated.

Experimental, semi-empirical and theoretical quantitative structure-retention relationship (QSRR) models were used to accurately predict retention factors (logk) in reversed-phase liquid chromatography (RPLC). These models are useful to characterize solvents for determination of the behavior and interactions of molecular structure and develop chromatographic methods. In both of QSPR and QSRR models using the PLS method, the first and second components captured main information which is related to van der Waals forces and polar interactions, and their results coincide with those from LSER.

The results showed that the models of physicochemical properties and retention factors (logk) in chromatographic system can be successfully developed by the PLS method. PLS models were able to predict physicochemical properties of organic compounds directly from theoretical descriptors without prior synthesis, measurement or sampling. Further, the PLS method could overcome colinearity in data sets, and it is therefore a rapid, cheap and highly efficient approach