In silico development, validation and comparison of predictive QSAR models for lipid peroxidation inhibitory activity of cinnamic acid and caffeic acid derivatives using multiple chemometric and cheminformatics tools.

The design and development of antioxidant molecules have lately gained a great deal of focus which is attributed to their immense biomedicinal importance in combating the free radical associated health hazards. In a situation to replenish the endogenous antioxidant loss, synthetic molecules with potent antioxidant activity is demanded. The present work thus aims at in silico modeling of antioxidant molecules that may facilitate in searching and designing of new chemical entities with enhanced activity profile. A series of cinnamic acid and caffeic acid derivatives having the ability to inhibit lipid peroxidation have been modeled in the present work. Three different types of models were developed using different chemometric and cheminformatics tools to identify the essential structural attributes: (a) descriptor based QSAR models, (b) 3D pharmacophore models and (c) HQSAR (hologram QSAR) models. For the conventional QSAR modeling, descriptors belonging to different categories [quantum chemical descriptors (Mulliken charges of the common atoms of the molecules), thermodynamic descriptors, electronic descriptors, structural descriptors and spatial descriptors] were calculated for the development of statistically significant as well as well interpretable quantitative structure-activity relationship (QSAR) models. Two different chemometric tools [genetic function approximation (GFA) and genetic partial least squares (G/PLS)] were employed for the development of the QSAR models. The 3D pharmacophore model focused on the essential pharmacophoric features while the HQSAR model implicated the prime structural fragments that were necessitated for the optimal anti-lipid peroxidative activity of the molecules. All the models were validated based on internal, external and overall validation statistics. Randomization was performed in order to ensure the absence of chance correlation in the developed models. Among all models, the descriptor-based model developed using the GFA-spline technique yielded the most satisfactory results. The results obtained from all the models corroborate well with each other and chiefly signify the importance of the ketonic oxygen of the amide/ acid fragment and the ethereal oxygen substituted on the parent phenyl ring of the molecules under study. Thus the models can efficiently be utilized for extensive screening of large datasets and their subsequent activity prediction.