Computational Strategies for Investigating Phytochemicals in Drug-resistant Tuberculosis

Tuberculosis (TB) is a serious global health concern precipitated by the pathogen Mycobacterium tuberculosis. The manifestation of drug-resistant forms of tuberculosis, such as extensively drug-resistant (XDR) and multidrug-resistant (MDR) strains, makes treatment more difficult.

Innovative methods are required to find novel therapies because traditional drug development procedures are expensive and time-consuming. So, computational phytochemistry, which utilizes computer-aided techniques to create and find novel medications from natural plant molecules, provides new opportunities for the discovery of antitubercular agents. (DR-TB). This review aims to critically evaluate the potential of computational phytochemistry in accelerating the discovery of phytochemical-based therapeutics against drug-resistant tuberculosis. Relevant literature was examined, focusing on computational approaches including molecular docking, virtual screening, quantitative structure- activity relationship (QSAR) analysis, molecular dynamics (MD) simulations, artificial intelligence- assisted screening, pharmacophore modeling, and ADMET prediction for identifying promising anti-TB phytochemicals targeting key Mycobacterium tuberculosis proteins.

The reviewed studies revealed that several classes of phytochemicals, including flavonoids, alkaloids, terpenoids, and phenolic compounds, demonstrated strong binding affinities toward validated drug-resistant TB targets such as InhA, DprE1, KasA, PknB, and DNA gyrase. Studies employing integrated computational workflows combining molecular docking, molecular dynamics simulations, and ADMET analyses provided more reliable predictions than docking-only approaches, facilitating the identification and optimization of potential anti-TB lead compounds.

Given the prevalence of DR-TB and the shortcomings of existing treatments, this strategy tackles the urgent need for novel treatments. By fusing the enormous diversity of plant chemicals with computational capacity, this research offers a viable path for the revelation and development of unprecedented natural product-based anti-TB medications.

Computational phytochemistry represents a promising and cost-effective strategy for prioritizing phytochemicals against MDR/XDR-TB. These encouraging in silico findings must be translated into successful treatments for drug-resistant tuberculosis, which will require more in vitro antimycobacterial screening, target-based biochemical assays, pharmacokinetic and toxicity studies, in vivo efficacy evaluation in tuberculosis models, and ultimately clinical validation. Future studies should also integrate artificial intelligence- driven drug discovery and multi-target screening approaches to improve hit identification and lead optimization for combating drug-resistant tuberculosis.