Structure-antitumor activity relationships of 9-anilinoacridines using pattern recognition

Abstract

A pattern-recognition analysis using the ADAPT system was performed on a set of 9-anilinoacridine antitumor agents to determine whether computer-generated descriptors could be used to separate active from inactive compounds. A training set of 213 compounds was chosen by random computer selection from a list of 776 structures. Maximal increase in life span at the LD10 dosage, a response which is difficult to model using traditional Hansch analysis, was used as the measure of biological activity. A set of 18 molecular descriptors including fragment, substructure environment and physicochemical property descriptors (molar refraction, partial electronic charge) was identified which could correctly classify 94% of the compounds in the training set (97% of active and 85% of inactive compounds). Eight of the inactive compounds that were misclassified contained amino substituents, suggesting a role for ionization. The weight vector that was obtained from the training set was applied to a prediction set of 50 compounds that were not included in the original analysis and to a set of 69 structures drawn from the recent literature. The prediction set results, ranging from 73-86% correct, were lower than those of the training set, but they clearly indicated that pattern-recognition techniques could be useful in the screening of proposed or already existing agents, and especially useful for the identification of active compounds.