This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.

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Immobilized Candida rugosa lipase was used for the synthesis of citronellyl laurate from citronellol and lauric acid. Screening of different types of support (Amberlite MB-1 and Celite) for immobilization of lipase and solvent (n-hexane, n-heptane, and iso-octane) and optimization of reaction conditions, such as catalyst loading, effect of substrates molar ratio and temperature, have been studied. The maximum enzyme activity was obtained at 310 K. The immobilized C. rugosa lipase onto Amberlite MB-1 support was found to be the best support with a conversion of 89% of citronellyl laurate ester in iso-octane compared to Celite 545. Deactivation of C. rugosa lipase at 313, 318 and 323 K were observed. Ordered bi bi mechanism with dead end complex of lauric acid was found to fit the initial rate data and the kinetic parameters were obtained by non-linear regression analysis.

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