Designing of Thiazolidinones for COVID-19 and its Allied Diseases: An In silico Evaluation

噻唑烷酮类药物在新冠肺炎及其相关疾病治疗中的应用设计:计算机模拟评估

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Abstract

In silico studies in terms of density functional theory (DFT), molecular docking, and ADMET (absorption, distribution, metabolism, excretion and toxicity) were performed for 55 thiazolidinones compounds derived from different amines and aldehydes. DFT is a computational quantum mechanical modeling method used to predict the various properties of the compounds. Different parameters such as Electronegativity (x), Chemical Hardness (ŋ), Chemical Potential (μ), Ionization potential (IP), and Electron Affinity (EA), etc. were calculated by Koopmans theorem. The compounds were docked with Molecular Operating Environment (MOE) software using already reported PDB files of BChE, AChE, and α-glucosidase. To analyze the Spike Glycoprotein of SARS-Cov-2 and heterocyclic compounds, molecular interactions study was carried out between Spike Glycoprotein of SARS-Cov-2 (6VXX) and 55 synthetic heterocyclic compounds. It was performed by the utilization of PyRx Virtual Screening Tool and AutoDock Vina based virtual environment was used in PyRx. Maximum binding affinity was observed with compound A7 which was -8.7 kcal/mol and then with A5 which was -8.5 respectively. In the case of the AChE enzyme, B5 has a maximum docking score of -12.9027 kcal/mol while C7 depicted the maximum score for the BChE enzyme with a value of -8.6971 kcal/mol. The docking studies revealed that C6 compound has maximum binding capacity toward glucosidase (-14.8735 kcal/mol). ADMET properties of under consideration compounds were determined by Swiss online-based software which concluded that these molecules have a drug-like properties and having no violation.

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