Thursday , 29 February 2024

Inslico-Prediction of Drug Solubility in Aquous Media by Theoritical Descriptors and QSAR Method

Elham Baher*, Poneh Ebrahimi, and Naser Darzi Naftchali
Faculty of science, Department of Chemistry, Golestan University, Gorgan, Iran. 

The most key factor in transition of drugs across the biological membrane is their solubility in water. In this study, a novel theoretical model was proposed for the prediction of drug solubility in the aqueous media (log 1/S) by using employing the molecular structure descriptors. The data set consists of 58 different drugs. Molecular descriptors were calculated and selected by genetic algorithm (GA) and stepwise-multiple linear regression (SMLR) methods. The selected descriptors with GA were: momentum of inertia, total molecular surface area, difference in CPSA, PPSA-3 atomic charge weighted PPSA, FPSA-3 fractional PPSA, count of H-donors sites, HA dependent HDSA-2/SQRT (TMSA) and kier shape index.  The selected descriptors with SMLR were: FPSA-3, Kier shape index, randic index, XY shadow and count of H-donors sites.  Then prediction of log 1/S as a criterion of drug delivery was accomplished by support vector machine (SVM) by using GA and SMLR selected descriptors, separately. By comparison of results obtained, it was concluded that the GA-SVM model was superior over other models. The statistical result presented a good generality and ability of SVM model in drug delivery property. The simplicity, reliability and high speed calculation are some advantages of the present work.   
Keywords: log 1/S, support vector machine, genetic algorithm, quantitative structure-activity relationship

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