Computational structure–activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production Original Research Article

Previously, we screened a series of arylcarboxylic acid hydrazide derivatives for their ability to induce macrophage tumor necrosis factor α (TNF-α) production and identified 16 such compounds. In the present study, we evaluated 23 additional arylcarboxylic acid hydrazides and found that seven of these compounds also induced macrophage TNF-α production, representing novel compounds with this activity. The total set of active compounds was then used for computational structure–activity relationship (SAR) analysis to further optimize lead molecules. A sequence of (1) linear discriminant analysis, (2) classification tree analysis with linear combination, and (3) univariate splits based on atom pair descriptors led to the derivation of SAR rule-based algorithms with fitting accuracy of 96.5%, 91.9%, and 84.9%, respectively. The SAR rules obtained from classification tree analysis with univariate splits, which was based on three atom pair descriptors only, revealed that the main factors influencing agonist activity of arylcarboxylic acid hydrazide derivatives were the presence of a methyl or trifluoromethyl group in the benzene ring attached to the furan moiety, an alkoxy group in the aromatic ring near the methylenehydrazide linker, and two or more halogen atoms (chlorine or bromine) on one side of the dumbbell-shaped hydrazide molecule opposed by an aromatic moiety on the opposite side of the molecule. Thus, these rules represent a relatively simple classification approach for de novo design of small-molecule inducers of macrophage TNF-α production.