Molecular docking towards drug discovery

Investigating the binding affinity, molecular dynamics, and ADMET properties of 2,3-dihydrobenzofuran derivatives as an inhibitor of fungi, bacteria, and virus protein

Background

2,3-Dihydrobenzofurans (DHB) have proposed as advantages structures, and used as chemical entresol to design small compound libraries. The present study illustrates to explore 2,3-dihydrobenzofurans(DHB) in comparison to selected some derivatives drugs by using molecular docking and molecular dynamics, as well as ADMET studies. The online database “Molinspiration online server” was used to detect the physicochemical pharmacokinetics and drug likeness score of DHB drugs. For estimation of molecular docking, six pathogens, such as Aspergillus niger (PDB id: 1kum), Candida albicans (3dra), Escherichia coli (6og7), Salmonella typhi (4k6l), Influenza (1ru7), and Hepatitis C (4tyd), were chosen due to close biological studies.

Results

From Molinspiration online server has showed that DHB did not violate the “Lipinski five rule” as drugs, leading compound for molecular docking exhibited the potential interaction to the active residue. The binding affinity of DHB2 (−7.00 kcal/mol) against 3dra was higher than DHB8 (−6.40 kcal/mol) and DHB (5.70 kcal/mol) for compounds. The results of molecular docking show that the compounds mentioned in this study are not equally effective against pathogens, such as fungi, viruses, and bacteria. However, DHB2, DHB3, and DHB 8 compounds can work against almost given pathogens which results are derived from auto dock vina in terms of binding affinity around 6.00 kcal/mol, and Fire Dock has values from about 38.0 to 42.0 kcal/mol. To explore the dynamic nature of the interaction, 50 ns molecular dynamics (MD) simulation was performed on the selected protein-DHB complexes. Thus, DHB 8 has greater potential to interact for further for fungi.

Conclusion

Finding from this study can play an effective role as a drug in any biological system. This study as well recommends to researchers to synthesize these DHBs for evaluation of its biological activity.

Graphical abstract

Novel AChE and BChE inhibitors using combined virtual screening, text mining and in vitro binding assays

In the current work, we developed a computational pipeline method for predicting the binding affinities of studied compounds at the specific target sites. Since many approved therapeutic compounds involve indole or indole-derivative rings, in the current study we focused compounds including these fingerprints. Initially, 212520 compounds were retrieved from Specs-SC library and after the conversion of IUPAC text file format, compounds that include 'indol' keyword (5194 compounds) were used in binary QSAR-based models to screen against a defined therapeutic activity "Alzheimer's disease" (AD). The molecules that have higher AD therapeutic activity values (>0.5) were then used in the 26 different toxicity-QSAR models. Binary QSAR models resulted 89 hits that have high AD therapeutic activity and no toxicity. Selected 89 molecules were then screened against acetylcholinesterase (AChE) targets using molecular docking and top-docking poses of compounds were used in initially short (10 ns) molecular dynamics (MD) simulations. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding free energy calculations were performed for 89 ligands and tightly bound 17 ligands based on average MM/GBSA scores were selected for long (100 ns) MD simulations. The same protocol was also applied for the known 4 AChE inhibitors. Selected hits were also docked to the binding pocket of butyrylcholinesterase (BChE). Finally, based on MM/GBSA scores, as well as their corresponding docking scores and metabolite production profiles, 7 compounds were selected and their in vitro tests were performed. Out of 7 compounds, 6 of them showed μM-level inhibition for both AChE and BChE targets.Communicated by Ramaswamy H. Sarma.