Leishmaniasis is a neglected disease caused by protozoa belonging to the Leishmania genus. The search for effective anti-Leishmanial compounds is challenging due to high costs, time requirements, and long-term treatment. However, today, with the help of computational approaches, we can undertake projects with lower costs and shorter timelines. The objective of this study was to discover novel and potent anti-Leishmanial compounds and establish the real relationship between the structure of these compounds and their biological activity. To achieve this goal, a total of 26 chemical compounds underwent QSAR analysis. After data preprocessing, a suitable set of molecular descriptors was calculated, and significant descriptors were selected. The Stepwise-MLR model identified nine important descriptors that influenced the activity of the chemical compounds: ” EEig01x,” ” IVDE,” ” G2m,” ” Mor16m,” ” JGI5,” ” E2e,” ” H0e,” ” G1p,” and ” RDF030m.” The developed model was evaluated based on the correlation between observed and predicted values (R=0.97), the correlation coefficient (R 2=0.94), and the root mean square error (RMSE=0.211). Additionally, molecular docking using AutoDock Vina was employed to explore the binding modes of 26 compounds with Leishmania major N-myristoyltransferase. The binding energy fit score of the best inhibitor, inhibitor 8, was –9.6 kcal/mol. For further investigation, the docked structure obtained from the AutoDock results underwent a molecular dynamics (MD) simulation for 100 ns. Analysis of the trajectory file confirmed the efficacy of the final compound against Leishmania. This study marks a significant step in the development of drug candidates against Leishmaniasis.

The global pandemic of coronavirus disease is attributable to a newly emerged coronavirus targeting the respiratory system. A pivotal approach in addressing this pandemic involves the search for pharmaceutical compounds capable of impeding the essential viral proteins necessary for viral replication and infection. Since ACE2 enzyme acts as the primary cellular entry point for certain coronaviruses, inhibiting it can play a crucial role in our efforts to combat these viruses. In this study, we employed a range of ACE2 inhibitors to conduct quantitative structure-activity relationship (QSAR) analyses, molecular docking analysis, and molecular dynamics simulations. The QSAR investigation, which relies on the Stepwise-MLR model developed in this study, provides a conceptual framework for improving the effectiveness of ACE2 inhibitor compounds. The molecular docking analysis revealed possible binding locations of the compounds within the ACE2 (PDB ID=6m0j). A 150-ns molecular dynamics simulation was conducted to validate the conformational stability of the top three promising compounds when bound to SARS-CoV-2-ACE2 complex. The findings indicated that all three compounds consistently occupied the specific binding pocket of the target and established numerous hydrogen bonds throughout the simulation, aligning with the results of the molecular docking studies. This research could help identify new leads for the development of novel anti-viral drugs against SARS-CoV-2.