Learn More
An accurate and generally applicable method for estimating aqueous solubilities for a diverse set of 1297 organic compounds based on multilinear regression and artificial neural network modeling was developed. Molecular connectivity, shape, and atom-type electrotopological state (E-state) indices were used as structural parameters. The data set was divided(More)
Based on the atom-type electrotopological state (E-state) indices, a quantitative structure-property relationship model for the prediction of aqueous solubility for a diverse set of 745 organic compounds is presented. The multiple linear regression analysis was used to build the models. A training set of 674 compounds, containing 349 liquids and 325 solids(More)
A group contribution method based on atom-type electrotopological state indices for predicting the biodegradation of a diverse set of 241 organic chemicals is presented. Multiple linear regression and artificial neural networks were used to build the models using a training set of 172 compounds, for which the approximate time for ultimate biodegradation was(More)
A group contribution approach based on atom-type electrotopological state indices for predicting the soil sorption coefficient (log KOC) of a diverse set of 201 organic pesticides is presented. Using a training set of 143 compounds, for which the log KOC values were in the range from 0.42 to 5.31, multiple linear regression (MLR) and artificial neural(More)
A method for predicting the aqueous solubility of drug compounds was developed based on topological indices and artificial neural network (ANN) modeling. The aqueous solubility values for 211 drugs and related compounds representing acidic, neutral, and basic drugs of different structural classes were collected from the literature. The data set was divided(More)
The ability of neural network models to predict aqueous solubility within series of structurally related drugs was evaluated. Three sets of compounds representing different drug classes (28 steroids, 31 barbituric acid derivatives, and 24 heterocyclic reverse transcriptase inhibitors) were studied. Topological descriptors (connectivity indices, kappa(More)
We describe robust methods for estimating the aqueous solubility of a set of 734 organic compounds from different structural classes based on multiple linear regression (MLR) and artificial neural networks (ANN) model. The structures were represented by atom-type electrotopological state (E-state) indices. The squared correlation coefficient and standard(More)
Quantitative structure-activity relationships (QSAR), based on the atom level E-state indices and calculated molecular properties (log P, MR), have been developed for the affinity of a large set of TIBO derivatives against HIV-1 reverse transcriptase (HIV-1 RT) utilizing multiple linear regression techniques. A model with five descriptors, including four(More)
Using a training set of 191 drug-like compounds extracted from the AQUASOL database a quantitative structure-property relationship (QSPR) study was conducted employing a set of simple structural and physicochemical properties to predict aqueous solubility. The resultant regression model comprised five parameters (ClogP, molecular weight, indicator variable(More)
  • J Huuskonen
  • 2001
The solubility of drugs in water is of central importance in the process of drug discovery and development from molecular design to pharmaceutical formulation and biopharmacy. The ability to estimate the aqueous solubility and other properties of a promising lead compound affecting its pharmacokinetics is a prerequisite to rational drug design, although it(More)