The distribution coefficient (log P) is an important molecular characteristic that allows us to estimate the lipophilicity of chemical compounds and predict how a drug will behave, fundamentally against the processes of absorption and excretion. The experimental determination of this and other properties of interest has several limitations, such as the high time invested and the consumption of considerable amounts of sample. In recent years, the development of new drugs has been supported by computational tools that allow a theoretical prediction of their properties from the information collected by their molecular descriptors, their design being much faster and cheaper. This paper shows the results of a structure-property relationship (QSPR) study aimed at finding a predictive mathematical model of the distribution coefficient of organic compounds of pharmaceutical interest. Through the computer programs ACDLabs (simplified molecular representations and calculation of log P) and MODESLAB (calculation of molecular descriptors) a training series consisting of 200 compounds classified in ten pharmacological groups was formed. Using the BuildQSAR computer program, an optimal prediction model of log P was obtained, considering the five molecular descriptors that best correlated with this property as independent variables. The model obtained showed a percentage of adjustment to the experimental data of 85%, as well as a standard error of the estimate lower than the logarithmic unit. Its internal validation showed an adjustment percentage of 80%.
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A QSPR model for the prediction of the partition coefficient of organic compounds of pharmaceutical interest
Published: 14 November 2019 by MDPI in MOL2NET'19, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 5th ed. congress UDLABIOTECH-01: I International Biotechnology Congress, UDLA, Quito, Ecuador, 2019
Keywords: QSPR, models, prediction, partition coefficient, pharmaceuticals