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Exploring the new therapeutic indications of known drugs for treating COVID-19, popularly known as drug repurposing, is emerging as a pragmatic approach especially owing to the mounting pressure to control the pandemic. Targeting multiple targets with a single drug by employing drug repurposing known as the polypharmacology approach may be an optimised strategy for the development of effective therapeutics. In this study, virtual screening has been carried out on seven popular SARS-CoV-2 targets (3CL pro , PL pro , RdRp (NSP12), NSP13, NSP14, NSP15, and NSP16). A total of 4015 approved drugs were screened against these targets. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected based on the docking score, ability to interact with four or more targets and having a reasonably good number of interactions with key residues in the targets. The MD simulations and MM-PBSA studies showed reasonable stability of protein-drug complexes and sustainability of key interactions between the drugs with their respective targets throughout the course of MD simulations. The identified four drug molecules were also compared with the known drugs namely elbasvir and nafamostat. While the study has provided a detailed account of the chosen protein-drug complexes, it has explored the nature of seven important targets of SARS-CoV-2 by evaluating the protein-drug complexation process in great detail. Graphical abstract Drug repurposing strategy against SARS-CoV2 drug targets. Computational analysis was performed to identify repurposable approved drug candidates against SARS-CoV2 using approaches such as virtual screening, molecular dynamics simulation and MM-PBSA calculations. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected as potential candidates. Supplementary Information The online version contains supplementary material available at 10.1007/s12039-022-02046-0.

Molecular Property Diagnostic Suite (MPDS TB ) is a web tool (http://mpds.osdd.net) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb). MPDS TB tool has nine modules which are classified into data library (1-3), data processing (4-5) and data analysis (6-9). Module 1 is a repository of literature and related information available on the Mtb. Module 2 deals with the protein target analysis of the chosen disease area. Module 3 is the compound library consisting of 110.31 million unique molecules generated from public domain databases and custom designed search tools. Module 4 contains tools for chemical file format conversions and 2D to 3D coordinate conversions. Module 5 helps in calculating the molecular descriptors. Module 6 specifically handles QSAR model development tools using descriptors generated in the Module 5. Module 7 integrates the AutoDock Vina algorithm for docking, while module 8 provides screening filters. Module 9 provides the necessary visualization tools for both small and large molecules. The workflow-based open source web portal, MPDS TB 1.0.1 can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular.

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