In Silico design and evaluation of novel anti-tubercular agents as Inha inhibitors through a virtual screening approach

Abstract

Tuberculosis remains a critical global health challenge, necessitating the urgent development of novel therapeutics. In this study, we employed an integrated computational approach to design and evaluate potent inhibitors targeting enoyl-acyl carrier protein reductase (InhA) in Mycobacterium tuberculosis . A robust 2D quantitative structure–activity relationship (QSAR) model was developed, demonstrating high predictive accuracy (R 2 = 0.966, Q 2 LOO = 0.957) and interpretability through descriptors AATSC6i, SCH-5, and maxdssC. Molecular docking studies identified compounds with superior binding affinities, notably Compound 14 (− 118.234 kcal/mol), which exhibited key interactions with active-site residues such as ALA191 and ILE215. Density functional theory (DFT) calculations provided insights into electronic properties and reactivity, confirming the stability of lead compounds. Drug-likeness and ADMET profiling revealed favourable pharmacokinetic properties, including high intestinal absorption and minimal toxicity risks. Based on its favourable binding profile and non-toxic ADMET properties, compound 14 was selected as a template for designing two novel derivatives. These analogues demonstrated improved docking scores (− 132.579 and − 125.894 kcal/mol), high intestinal absorption (> 88%), and no predicted toxicity, underscoring their potential as effective InhA inhibitors. Molecular dynamics simulations over 250 ns further validated the stability and binding modes of top candidates, with MM/GBSA calculations highlighting the significance of van der Waals and hydrophobic interactions. These findings position compound 14 and its derivatives as promising candidates for further preclinical development in tuberculosis therapy.