Title : In silico analysis of isomeric glycolipids from sterculia setigera and its derivatives as lead compounds against mycobacterium tuberculosis target
Abstract:
Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains one of the leading infectious diseases worldwide, posing a major public health challenge due to the rise of drug-resistant strains and limitations of current treatment regimens. The urgent need for novel, effective, and safer therapeutic agents has driven the application of computational approaches to accelerate drug discovery. In this study, an integrated in silico strategy was employed to identify potential anti-tubercular compounds with promising efficacy and safety profiles. A series of candidate ligands were screened using molecular docking against two critical M. tuberculosis protein targets—3HI7 (Mycobacterial DNA gyrase) and 1F0N (Enoyl-ACP reductase, InhA). Docking analyses were conducted using Molegro Virtual Docker to evaluate binding affinities and interaction profiles compared to standard anti-TB drugs such as isoniazid, ethambutol, pyrazinamide, and levofloxacin. The most promising ligands, particularly Ligands 1, 2, 5, and 7, exhibited significantly stronger binding affinities and more extensive hydrogen bonding and hydrophobic interactions than the reference drugs, suggesting enhanced target engagement.In parallel, pharmacokinetics and toxicity assessments were performed using SwissADME and ProTox-3.0 to evaluate drug-likeness, ADMET properties, and potential toxicological liabilities. The ligands generally complied with key drug-likeness rules and demonstrated favorable solubility, lipophilicity, and metabolic profiles, with low predicted toxicity risks across major organ systems and molecular pathways. This integrated computational study highlights Ligands 1, 2, 5, and 7 as promising lead compounds for further development as anti-tubercular agents. Their superior binding efficiency, favorable pharmacokinetic characteristics, and predicted safety support continued investigation through experimental validation and optimization.
