Exploring Antimalarial Activity of Chalcone Derivatives through QSAR - Neerja Shukla; Anita Singh; Rahila Khan; Shashikant Verma; Anushka Shukla; Kumari Suman; Bechan Sharma (International Journal of Bioinformatics and Computational Biology, 2025)
Background: The core structure of chalcones contains a reactive α,β-unsaturated system within the aromatic rings, which plays a key role in mediating various biological effects. These effects include enzyme inhibition, anticancer activity, anti-inflammatory properties, as well as antibacterial, antifungal, antimalarial, antiprotozoal, and anti-filarial actions.Modifying the structure by introducing substituent groups to the aromatic ring can enhance potency, reduce toxicity, and expand their range of pharmacological actions. Methods: A total of twenty-seven chalcone derivatives were analyzed for their physicochemical and molecular properties. Chalcones exhibit a broad spectrum of pharmacological properties, encompassing antibacterial, antifungal, anti-inflammatory, and anticancer activities, making them promising candidates for therapeutic development, and molecular connectivity (χ), were computed using E-Dragon software.The software primarily accepts molecular file formats such as SMILES notations, which were generated online using Babel software. These 2D structures were then converted into 3D optimized structures using CORINA, an online tool provided by Molecular Networks GmbH. Furthermore, the 3D structures of the compounds were constructed using GaussView software to calculate various quantum chemical descriptors based on density functional theory (DFT). The analysis included key values such as total energy, softness, hardness, chemical potential, and the energy levels of the molecule’s outermost electrons — specifically, the highest occupied (HOMO) and lowest unoccupied (LUMO) orbitals. All the compounds were completely optimized in the gas phase using the 6-31+G* basis set for accurate structural calculations. To ensure that the optimized structures represented true energy minima, harmonic vibrational frequency calculations were carried out—confirmed by the presence of only positive vibrational frequencies. Results: A comprehensive array of molecular descriptors—including D, ID, IOR, Log P, Mr, Mv, Mw, Pc, BAC, Pz, St, W, Wa, 0c, 1c, 2c, 3c, 4c, 5c, and Xeq—has been recognized as crucial for effectively modeling the biological activity of the investigated compounds. These descriptors serve as fundamental parameters in capturing the structural and physicochemical complexities necessary for accurate predictive analysis.QSAR Model 2, mathematically represented by the following equation:pIC50 = -20.605 (±6.600) IOR - 0.747 (±0.454) I1 - 5.083 (±3.478) Xeq + 51.647This model was systematically refined using empirical parameters to enhance its predictive accuracy. Each coefficient in the equation signifies the relative influence of the corresponding molecular descriptor on the predicted biological activity. The negative values associated with IOR, I1, and Xeq indicate an inverse correlation between these descriptors and the pIC50 values, suggesting their role in modulating compound potency. The optimization process involved fine-tuning these parameters to achieve the best fit with the experimental data, ensuring a robust and reliable depiction of the structure-activity relationship. Conclusions: The QSAR model suggests that substituents with lower refractive indices and less electronegative groups improve activity. In contrast, indomethacin derivatives containing a -CH2CH2NHCONH(CH2)3ONO2 group at the R1 position negatively impact activity.The findings are critically analyzed using regression data and cross-validation techniques. The Poglani factor (Q) and results from the LOO (leave-one-out) method validate the reliability and predictive accuracy of the proposed models, offering valuable insights for designing new analogues with improved potency in the future.
Keywords: Anti-inflammatory Activity; Indomethacin Derivatives; LOO (Leave-One-Out); Poglani Factor (Q); and Potency