AQUEOUS SOLUBILITY PREDICTION OF DRUGS BASED ON MOLECULAR TOPOLOGY AND NEURAL-NETWORK MODELING

A method for predicting the aqueous solubility of drug compounds was d eveloped based on topological indices and artificial neural network (A NN) modeling. The aqueous solubility values for 211 drugs and related compounds representing acidic, neutral, and basic drugs of different s tructural classes were collected from the literature. The data set was divided into a training set (n = 160) and a randomly chosen test set (n = 51). Structural parameters used as inputs in a 23-5-1 artificial neural network included 14 atom-type electrotopological indices and ni ne other topological indices. For the test set, a predictive r(2) = 0. 86 and s = 0.53 (log units) were achieved.