The absorption of a drug compound through the human intestinal cell lining is an important property for potential drug candidates. Measuring this property, however, can be costly and time-consuming. The use of quantitative structure-property relationships (QSPRs) to estimate percent human intestinal absorption (%HIA) is an attractive alternative to experimental measurements. A data set of 86 drug and drug-like compounds with measured values of %HIA taken from the literature was used to develop and test a QSPR mode. The compounds were encoded with calculated molecular structure descriptors. A nonlinear computational neural network model was developed by using the genetic algorithm with a neural network fitness evaluator. The calculated %HIA (cHIA) model performs wells, with root-mean-square (rms) errors of 9.4%HIA units for the training set, 19.7%HIA units for the cross-validation (CV) set, and 16.0%HIA units for the external prediction set.

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Multiple linear regression analysis and computational neural networks are used to develop models that predict reduced ion mobility constants (KO) from quantitative structural information encoded as descriptors. The errors associated with the models are similar to the calculated experimental error of -0,040 KO units. The best regression model contains five descriptors and has a multiple correlation coefficient (R) value of 0.991 and a standard deviation of 0.0469 KO units. The neural network model utilizes the same five descriptors and has a root mean square (RMS) error of 0.0393 KO units. The descriptors encode molecular size, weight, functional group, and structural classifications. (18) Jansson, P. A.