Identification of novel fructose 1,6-bisphosphate aldolase inhibitors against tuberculosis: QSAR, molecular docking, and molecular dynamics simulation-based analysis of DrugBank compounds

Global initiatives aim to curb tuberculosis (TB) by developing efficient vaccines and drugs against Mycobacterium tuberculosis (M. tb). The pressing need for innovative and swift anti-TB drug screening methods, due to the drawbacks of traditional approaches, is met by employing Structure-based virtual screening (SBVS) and machine learning (ML) in drug discovery. The present study utilizes these methods to repurpose compounds from the DrugBank database (DBD) as anti-TB drugs, explicitly targeting the enzyme fructose-1,6-bisphosphate aldolase (FBA) in glycolysis and gluconeogenesis pathways.Five classifiers, including REPTree, Decision Stump, Random Tree, Random Forest, and J48evaluate training data against M. tbFBA. AdmetSAR 2.0 assesses drug-like properties and toxicity of ML-identified compounds using four filters. Out of 9213 DBD compounds, 5280 were predicted as TB-active. REPTree, chosen for further screening, led to the identification of four promising preclinical anti-TB drug candidates from DrugBank—Serdemetan, Parecoxib, N, N-Diethyl-2-[(2-Thienylcarbonyl) amino], and Visnadine.All screened ligands show stable binding behaviour during a 200-ns molecular dynamics simulation. Density functional theory (DFT) analysis was also employed for the analysis HOMO (highest occupied molecular orbital)/LUMO (lowest unoccupied molecular orbital) gap, and both screened hits showed efficient results. This study presents a potential avenue for effective TB therapeutics development from compounds with proven druggability in other contexts.