TABLE 1.
List of some compounds discovered to target tuberculosis bacteria by using approaches of QSAR, pharmacophore modeling, and molecular docking.
| Compound | Database | QSAR Method | Descriptor calculation | Method validation | Pharmacophore modeling | Molecular docking software | Result | Ref |
|---|---|---|---|---|---|---|---|---|
| Xanthone derivatives as Anti TB agent | Protein data bank | Multiple linear regression (MLR) backward methods | Parameterized Model Number 3 (PM3), Austin Model 1 (AM1), and Density Functional Theory (DFT), Hartree-Fock (HF) | -- | CHIMERA 1.9 and ChemOffice®2015 | The 3,6 dihydroxy and 1,3,6 trihydroxy xanthone derivatives have good anti-tuberculosis activity when added to amide, sulfoxide, and carboxylate groups. A docking study was used to identify the inhibitory mechanism known as Kasa inhibitor, which is located in the cell wall of Mycobacterium TB. | Yuanita et al. (2020) | |
| Sulfathiazole Analogs such as mycobacterium tuberculosis h37rv Inhabitors | Antituberculosis drug discovery databases (Substructure mining tool Schrodinger et al., 2010) | Principal Component Regression (PCR) Analysis, Multiple linear regression (MLR), Partial Least Squares (PLS) Regression Analysis | Vlife MDS | External validation by predicting the activity of each molecule in the test set Internal validation (Leave-one-out) | -- | -- | Compared to the other two methods in predicting the antituberculosis H37RV inhibitor effect of sulfathiazole analogs, PLS analysis demonstrated significant predictive power and reliability | Vastrad (2012) |
| Amino-pyrimidine derivatives as Mycobacterium tuberculosis Protein Kinase B inhibitors | Literature based on the biological assay method | MLR | -- | Internal and external validation | -- | -- | With an excellent statistical fit, the QSAR model was created using MLR. Antituberculosis action was discovered to be influenced by physicochemical molecular and quantum descriptors. The researchers concluded that a model like this might be used to predict the antituberculosis action of these compounds | Chapman et al. (2012) |
| The pharmacophore model was used as a tool to identify a novel inhibitor of Mtb-DapB, a validated mycobacterial drug target | The ZINC natural product subsets and Asinex screening library | X-ray Crystal structure 1C3V | -- | -- | e-Pharmacophore option from the Phase module of the Schrodinger Suite | It was discovered that hybrid dynamic pharmacophore models created by employing a computation-based technique to screen compounds for new chemotypes, higher binding affinities, and drug-like features outperformed traditional models made from native ligands. Based on cheminformatics-based structure comparison, docking scores, binding energies, and ADMET properties, the compounds screened by the hybrid pharmacophore models were discovered to be more druglike, defining the hybrid models as useful tools for exploring novel anti-TB chemical space | Choudhury and Bhardwaj, (2020) | |
| Predicting the activity of 1,2,3-triazole and pyrazolopyridones as DprE1 inhibitor antitubercular agents | Literature | MR, Principal PCR, PLSR and PLS-SE) the method used to develop 4 QSAR models | V-life MDS | Internal and external validation | The MolSign module in VLifeMDS | The Biopredicta tool of V-Life MDS software version 4.6 | Utilizing pharmacophore modeling, QSAR analysis, molecular docking, and in silico ADME prediction, the function of 1,2,3-triazole and pyrazolopyridones as DprE1 inhibitors antitubercular drugs were explored, offering input into the structural foundation and inhibitory mechanism represents the group of substances serving as DprE1 antitubercular agents | Panigrahi et al. (2020) |
| Quinoline Schiff bases as enoyl acyl carrier protein reductase inhibitors | Literature | CoMFA, CoMSIA, and topomer CoMFA 3D structure of quinoline scaffold using molecular modeling software package SYBYL-X 2.0 | -- | -- | -- | Surface docking | The study proved that the presence of the -CH = N- and quinoline rings are critical for anti-TB activity. It was also discovered that compounds had a higher lipophilic character, making them capable of demonstrating positive biological activities. The reported models could be further investigated to develop newer, more powerful anti-TB drugs | Joshi et al. (2014) |
| QSAR and docking studies of pyrimidine derivatives against M. tuberculosis H37Rv | -- | MLR, Stepwise selection of Terms (SW) | -- | -- | -- | AutoDock | The study’s findings suggested that modifying and substituting the pyrimidine ring could result in a possible lead chemical with antibacterial activity and good docking. The findings of QSAR and docking studies on pyrimidine derivatives will aid the introduction of innovative antituberculosis medications | Hussain et al. (2016) |
| QSAR-driven Design, Synthesis, and Discovery of Potent Chalcone Derivatives with Antitubercular Activity | Bioassay, PubChem, SciFinder database, ChEMBL, also from literature | Avalon fingerprints, combined with support vector machine (SVM) gradient boosting machine (GBM), and random forest (RF) machine learning methods, MACCS, AtomPair, Morgan, FeatMorgan | -- | -- | -- | -- | Identifying novel and promising anti-TB drugs were made possible by integrating into silico design a QSAR-driven pathway for screening, production, and experimental evaluation. Thirty-three chalcone derivatives were created and evaluated against Mycobacterium tuberculosis strains. The synthesized chalcone compounds were proven effective against mono-resistant M. TB strains of isoniazid and rifampicin. The compounds were not harmful to mammalian (VERO) cells and appeared to be mycobacteria-specific, with just a little effect on S. aureus | Gomes et al. (2017) |