Quantum Chemical Calculation and in silico Molecular Modelling Studies on Some Multi-targeting Anti-Inflammatory Inhibitors
Ragini Kushwaha *
Department of Bioinformatics, Mahila Mahavidyalaya, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India.
Sandeep Pokharia
Department of Chemistry, Mahila Mahavidyalaya, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India.
Nilanjana Mani
Department of Bioinformatics, Mahila Mahavidyalaya, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India.
Albela Minj
Department of Bioinformatics, Mahila Mahavidyalaya, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
Problem Statement: Inflammation plays a key role in many chronic diseases, including autoimmune disorders and arthritis. There are some of the natural compounds, which possess anti-inflammatory properties and offer significant therapeutic potential. Computer-aided molecular design helps to discover the Quantitative Structure Activity Relationship (QSAR) of the molecules.
Aims: This study aims to provide a deeper understanding of the electronic structure and reactivity behaviour of natural compounds to design novel leads with enhanced biological activity.
Methodology: Natural anti-inflammatory molecules from plant and marine sources were identified through an extensive literature review. Compounds such as Oleocanthal, Viridicatin, Liquiritin, and Nobiletin were selected for analysis. Their electronic structures were studied using Density Functional Theory (DFT) with the B3LYP functional and 6-311G(d,p) basis set to calculate geometric and electronic parameters, reactivity descriptors, and molecular electrostatic potential (MEP) maps. Physicochemical properties and drug-likeness were assessed through in silico ADMET studies. Finally, molecular docking simulations were performed to evaluate the binding affinities and interactions of the selected compounds with key inflammatory targets, including IL-17, MAPK, TNF-α, and MMP-9.
Results: The DFT calculations revealed critical insights into the electronic distribution and reactivity patterns of the compounds, identifying potential interaction sites. ADMET predictions confirmed favourable drug-likeness and safety profiles, while docking studies highlighted significant binding affinities, particularly for Liquiritin, which demonstrated the highest binding efficiency among the tested compounds.
Conclusion: By integrating quantum chemical calculations and molecular modelling, this study provides a comprehensive framework for exploring the QSARs of natural anti-inflammatory compounds. The findings contribute to the rational design and discovery of novel anti-inflammatory drugs with improved therapeutic potential.
Keywords: Quantum chemical calculation, ADMET, molecular docking, anti-inflammatory natural compounds