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Nowadays, human health, as well as environment, are at risk due to uncontrolled usage on natural resources. Groundwater is one of the crucial natural resource, excessively used and contaminated by human beings. It is, majorly contaminated by anthropogenic activities. Such contaminated water, when used for drinking purpose, may cause serious effects on human health. Therefore, it is necessary to precisely estimate the quality of groundwater. Conventionally, the WQI, a weighted arithmetic index method, is commonly used to estimate groundwater quality by researchers. As time rolled on, some drawbacks, such as uncertainty of data, of WQI came into light. So researchers tried to find a new approach to mitigate the problems associated with WQI. In this direction, fuzzy logic has been used and proven by researchers to eliminate the ambiguity involved in qualitative and quantitative researches. The paper aims to do a comparative study between fuzzy logic and WQI to estimate groundwater quality. The objective is to represent complex groundwater data into clear and simple data that can be easily interpreted by the general public and policymakers.

Graphical abstract Captions: Virtual screening and docking workflow and schematic genomic representation of spike protein. HTVS (High through virtual screening); SP (Standard precision); XP (Extra precision); MDS (Molecular dynamics simulation). S1 (spike protein subunit-1); S2 (spike protein subunit-2); NTD (N terminal domain); RBD (Receptor binding domain); FP (Fusion peptide); HR1 (Heptapeptide repeat sequence 1); HR2 (Heptapeptide repeat sequence 2); TM (Transmembrane domain); CT (Cytoplasm domain). ACE2 (Angiotensin-converting enzyme2); serine protease (TMPRSS2); Bio-0597 and Bio-0357 (Bioactive compounds)., The scientific community is continuously working to discover drug candidates against potential targets of SARS-CoV-2, but effective treatment has not been discovered yet. The virus enters the host cell through molecular interaction with its enzymatic receptors i.e., ACE2 and TMPRSS2, which, if, synergistically blocked can lead to the development of novel drug candidates. In this study, 1503 natural bioactive compounds were screened by HTVS, followed by SP and XP docking using Schrodinger Maestro software. Bio-0357 (protozide) and Bio-597 (chrysin) were selected for dynamics simulation based on synergistic binding affinity on S1 (docking score −9.642 and −8.78 kcal/mol) and S2 domains (-5.83 and −5.3 kcal/mol), and the RMSD, RMSF and Rg analyses showed stable interaction. The DFT analysis showed that the adsorption of protozide/chrysin, the band gap of protozide/chrysin-F/G reduced significantly. From SERS, results, it can be concluded that QDs nanocluster will act as a sensor for the detection of drugs. The docking study showed Bio-0357 and Bio-0597 bind to both S1 and S2 domains through stable molecular interactions, which can lead to the discovery of new drug candidates to prevent the entry of SARS-CoV-2. This in-silico study may be helpful to researchers for further in vitro experimental validation and development of new therapy for COVID-19.