The membrane-anchored spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a pivotal role in directing the fusion of the virus particle mediated by the host cell receptor angiotensin-converting enzyme 2 (ACE-2). The fusion peptide region of the S protein S2 domain provides SARS-CoV-2 with the biological machinery needed for direct fusion to the host lipid membrane. In our present study, computer-aided drug design strategies were used for the identification of FDA-approved small molecules using the optimal structure of the S2 domain, which exhibits optimal interaction ratios, structural features, and energy variables, which were evaluated based on their performances in molecular docking, molecular dynamics simulations, molecular mechanics/generalized Born model and solvent accessibility binding free energy calculations of molecular dynamics trajectories, and statistical inferences. Among the 2,625 FDA-approved small molecules, chloramphenicol succinate, imipenem, and imidurea turned out to be the molecules that bound the best at the fusion peptide hydrophobic pocket. The principal interactions of the selected molecules suggest that the potential binding site at the fusion peptide region is centralized amid the Lys790, Thr791, Lys795, Asp808, and Gln872 residues. IMPORTANCE The present study provides the structural identification of the viable binding residues of the SARS-CoV-2 S2 fusion peptide region, which holds prime importance in the virus’s host cell fusion and entry mechanism. The classical molecular mechanics simulations were set on values that mimic physiological standards for a good approximation of the dynamic behavior of selected drugs in biological systems. The drug molecules screened and analyzed here have relevant antiviral properties, which are reported here and which might hint toward their utilization in the coronavirus disease 2019 (COVID-19) pandemic owing to their attributes of binding to the fusion protein binding region shown in this study.

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Open-source MD simulation tools provide academics and low-income countries with the ability to compete in drug discovery advancements. Gromacs is a well-known and established MD simulation tool, among others. Although command-line tools offer full flexibility to users, they require expertise and familiarity with the UNIX operating system. In this context, we have developed an automated bash workflow that enables users with minimal knowledge of UNIX or command-line tools to run protein/protein–ligand complex simulations bridged to MM/PBSA calculations. The workflow provides information to the user using Zenity widgets and requires minimal intervention, such as energy minimization, simulation duration, and output file naming. It initiates MD simulations within a few seconds (energy minimization, NVT, NPT, and MD) after taking input files and parameters, which takes 20–30 min in a command-line-based protocol. The single workflow also helps users to produce reproducible research results with fewer errors. The workflow is available at the GitHub repository: .

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