Chemical bonds are central to chemistry, biology, and allied fields, but still, the criterion to characterize an interaction as a non-covalent bond has not been studied rigorously. Therefore, in this study, we have attempted to characterize the non-covalent bonds by considering a total of 85 model systems depicting different chemical bonds comprising 43 non-covalent and 42 other bonds such as covalent, ionic, and coordinate bonds. The characterization has been done based on interaction energy, energy decomposition analysis (EDA), the NCI plot, and the analysis of topological properties of electron density. The interaction energy values, energy decomposition analysis, and NCI plot give insights into the full understanding of bond strength and its nature, but they fail to distinctively characterize the interaction as a non-covalent bond. Herein, a special criterion has been developed based on the topological parameters to characterize an interaction as a non-covalent bond. Topological parameters illustrate that the values of both ∇²ρ and H(r) are positive with a value of ρ < 0.03 a.u. and [−G(r)/V(r)] ≥ 1.00 for the non-covalent bonds. The value of ρ increases up to 0.06 a.u. with a positive value of ∇²ρ and a negative value of H(r) if the non-covalent bond is partially covalent in nature. The analysis of G(r) suggests that it dominates the H(r) of non-covalent bonds with greater than 50% contribution, whereas the contribution of G(r) varies between 35 and 50% in the case of bonds which are partially covalent in nature. The criterion based on topological parameters is likely to be very helpful to comprehend and ascertain the non-covalent bonds in the chemical as well as complex biological systems.

Molecular Property Diagnostic Suite (\(\text {MPDS}^{\mathrm{TB}}\)) is a web tool ( http://mpds.osdd.net ) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb). \(\text {MPDS}^{\mathrm{TB}}\) 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, \(\text {MPDS}^{\mathrm{TB}}\) 1.0.1 can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular.

The identification of new viral drugs has become a task of paramount significance due to the frequent occurrence of viral infections and especially during the current pandemic. Despite the recent advancements, the development of antiviral drugs has not made parallel progress. Reduction of time frame and cost of the drug development process is the major advantage of drug repurposing. Therefore, in this study, a drug repurposing strategy using molecular modelling techniques, i.e. biological activity prediction, virtual screening, and molecular dynamics simulation was employed to find promising repurposing candidates for viral infectious diseases. The biological activities of non-redundant (4171) drug molecules were predicted using PASS analysis, and 1401 drug molecules were selected which showed antiviral activities in the analysis. These drug molecules were subjected to virtual screening against the selected non-structural viral proteins. A series of filters, i.e. top 10 drug molecules based on binding affinity, mean value of binding affinity, visual inspection of protein-drug complexes, and number of H-bond between protein and drug molecules were used to narrow down the drug molecules. Molecular dynamics simulation analysis was carried out to validate the intrinsic atomic interactions and binding conformations of protein-drug complexes. The binding free energies of drug molecules were assessed by employing MMPBSA analysis. Finally, nine drug molecules were prioritized, as promising repurposing candidates with the potential to inhibit the selected non-structural viral proteins. Communicated by Ramaswamy H. Sarma