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In the present work we have calculated several DFT reactivity descriptors for 1,2,4,5-Tetrazine at the B3LYP/6–311++G(d,p) level of theory in order to analyze its reactivity in vacuum and solvent phases. Whereas, the influence of the solvent was taken into account employing the PCM model. DFT-based descriptors such as (electronic chemical potential, electrophilicity, condensed Fukui function….) have been determined to predict the reactivity of 1,2,4,5-Tetrazine. A series of eighteen 1,2,4,5-Tetrazine derivatives was studied by using two computational techniques, namely, quantitative structure activity relationship (QSAR) and molecular docking. QSAR models of the antitumor activity of some 1,2,4,5-Tetrazine derivatives were established in gas and solvent phases which exhibited good statistical values for both cases. Whereas, multiple linear regression (MLR) procedure was used to obtain the best QSAR models and the leave-one-out (LOO) method to estimate the predictivity of our models. The most and the least active compounds were docked with the protein (3C4E) to confirm those obtained results from QSAR models and elucidate the binding mode between this type of compounds and corresponding protein.

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QSAR studies have been performed on nineteen molecules of 1,2-dithiole-3-thione analogues. The compounds used are the potent inducers of enzymes involved in the maintenance of reduced glutathione pools as well as phase-2 enzymes important to electrophile detoxication. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and detoxication properties of the 1,2-dithiole-3-thione derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: qS2, qC3, qC5, qS6, DM, Pol, log P , MV, SAG, HE and E HOMO for the specific activity of quinone reductase; qS1, qS2, qC3, qC4, qC5, qS6, DM, Pol, log P , MV, SAG, HE and E HOMO for the production of growth hormone. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models.