2D-QSAR modeling and two-fold classification of 1,2,4-triazole derivatives for antitubercular potency against the dormant stage of Mycobacterium tuberculosis - Aher, Rahul Balasaheb; Sarkar, Dhiman (Molecular Diversity, 2021)
The dormant or latent form of Mycobacterium tuberculosis (MTB) is not killed by the conventional antitubercular drugs. The treatment of latent TB is essential to reduce the period of treatment as well as incidences of drug resistance. In this background, we have made an attempt to develop the quantitative structure-activity relationship models (QSAR: regression and classification based) against the dormant form of MTB and later used the developed classifier models (linear discriminant analysis (LDA) and random forest (RF)) for the two-fold classifications. The logic of applying this concept of two-fold classification for the MTB modeling is to increase the confidence of correct classification. The 2D-QSAR modeling suggested the contribution of burden eigen, edge adjacency, van der Waals (vdW) surface area, topological charge, and pharmacophoric indices in predicting the antitubercular activity against the dormant MTB. The prediction qualities of the training and test sets were found to be moderate and good, according to the mean absolute error (MAE)-based criteria's. The LDA and RF models unveiled the importance of burden eigen, edge adjacency, Geary autocorrelation, and drug-like indices as discriminating features to differentiate the antitubercular compounds into higher and lower active groups. The LDA model showed the classification accuracies of 85.14% and 87.10% for the training and test sets, while the RF model exhibited the accuracies of 100.00% and 80.65% for both the sets. The descriptors selected in the final models are only two-dimensional (2D), which are easy to compute and does not require computationally expensive steps of structure conversion, optimization, and energy minimization mandatorily needed before the computation of 3D descriptors. These models could be used for identifying and selection of higher active compounds against the dormant form of the MTB.
Keywords: Classification models; Dormant TB; Linear discriminant analysis (LDA); Mycobacterium tuberculosis (MTB); Nonlinear modeling; QSAR; Random forest (RF)

Online tools and antiviral databases for the development of drugs against coronaviruses - Aher, Rahul Balasaheb; Sarkar, Dhiman (In Silico Modeling of Drugs Against Coronaviruses: Computational Tools and Protocols, 2021)
The current crisis of coronavirus pandemic has created an urgent need for readily available scientific information to the researchers, students, professionals, and journalists. The scientific information for the research is costly, and most of the universities and research institutes cannot afford to subscribe to all the coronavirus-related journals/articles during this crisis time. In order to expedite the process of vaccine development and discovery of anti-COVID drugs, most of the pharmaceutical companies, research institutes and publishers are playing a key role and working on war footing to get either a vaccine or an anti-COVID drug as early as possible. The collective efforts are required from everyone in this testing time of the corona crisis. To provide our contribution to the scientific community, we have made here an attempt to give an overview of some of the tools and resources freely available that probably provide some insights in data mining and screening of novel lead molecules toward coronavirus. We have collected and compiled the information of open access online tools and antiviral databases essential for the discovery and development of corona vaccine and anti-COVID drugs. The open access tools include Open Educational Resources (OERs), Google cloud, online prediction server, web-based viewer, etc., while the antiviral databases include libraries of synthetic and untested compounds, antiviral drug databases, antiviral peptides, etc. The information presented in this chapter will help the researchers to use them directly in their projects of coronavirus drug discovery.

Pharmacophore modeling of pretomanid (PA-824) derivatives for antitubercular potency against replicating and non-replicating Mycobacterium tuberculosis - Aher, Rahul Balasaheb; Sarkar, Dhiman (Journal of Biomolecular Structure & Dynamics, 2021)
Pretomanid (PA-824) is the recently (2019) approved drug for the treatment of extensively drug-resistant (XDR) TB and the multidrug-resistant (MDR) TB by US FDA. The experimental data of antitubercular activity of 543 pretomanid derivatives (total 6 datasets) against replicating (active) and non-replicating (dormant) forms of Mycobacterium tuberculosis (strain H37Rv) are available in the literature. Such vast experimental data of pretomanid derivatives against both of these endpoints, and recent approval of pretomanid molecule as a drug encouraged us to utilize this existing experimental information for the development of the 3D-pharmacophore models. The developed model (Hypo-1, MABA) showed the three physicochemical features namely, the oxygen atom of nitro group (HBA_1), fused pyran ring of imidazopyran heterocycle (HYAl_2) and the 4-fluorophenyl moiety (HYAr_3) are crucial for the antitubercular activity against replicating M. tb. Subsequently, the pharmacophore model (Hypo-1, LORA) developed against the non-replicating form of M. tb also showed the contribution of three physicochemical features namely, the 4-tri-fluoromethyl group (HYAl_2) and both the phenyl groups (HYAr_3, HYAr_4) of biaryl moiety in increasing the antitubercular activity. Both the pharmacophoric classifier models showed the classification accuracies of 82.98 and 74.42% for the training set compounds, and 63.91 and 61.60% for the test set compounds respectively, for labelling the compounds into higher and lower active classes. Both the models were also found to be retaining the higher active compounds in top 1.00% of the total number of compounds (decoys and actives), after performing the decoy set screening. Communicated by Ramaswamy H. Sarma
Keywords: LORA assay; MABA assay; Mycobacterium tuberculosis; non-replicating M; pharmacophore modeling; pretomanid derivatives; replicating M; tuberculosis; virtual screening