Objective: Investigate the molecular docking of tamoxifen to identify its binding interactions with the estrogen receptor (ER) and assess its potential as an inhibitor.

Key Points:

• Used AutoDock Vina to dock tamoxifen into the active site of the estrogen receptor (ERα).

• Best docking pose exhibited a binding energy of -8.7 kcal/mol, forming hydrogen bonds with Glu353 and Arg394, along with hydrophobic interactions stabilizing the complex.

• Conducted Molecular Dynamics (MD) simulations using GROMACS to evaluate the stability of the tamoxifen-ERα complex over 100 ns.

Conclusion: Results confirm strong binding interactions, supporting tamoxifen’s established role as a selective estrogen receptor modulator (SERM) and providing insights for further structure-based drug optimization.

Objective: Perform a computational study to evaluate the inhibition of acetylcholinesterase (AChE) using molecular docking and molecular dynamics simulations.

Key Points:

• Used AutoDock Vina to dock a library of small molecules into the AChE active site.

• Best scoring compound exhibited a binding energy of -10.4 kcal/mol, forming hydrogen bonds with Ser203 and His447, key residues in the catalytic triad.

• Conducted Molecular Dynamics (MD) simulations using GROMACS to assess the stability of the ligand-enzyme complex over 100 ns.

Conclusion: Identified promising AChE inhibitors with stable binding interactions, supporting their potential for further in vitro validation in neurodegenerative disease research.

Objective: Perform a virtual screening study to identify potential kinase inhibitors using molecular docking and molecular dynamics simulations.

Key Points:

• Used AutoDock Vina to screen a library of kinase inhibitors against the ATP-binding site of a target kinase.

• Top-ranked compounds exhibited binding energies below -9.5 kcal/mol, forming key hydrogen bonds with hinge-region residues and additional stabilizing hydrophobic interactions.

• Followed up with Molecular Dynamics (MD) simulations using GROMACS to evaluate the stability and conformational dynamics of the ligand-kinase complexes over 100 ns.

Conclusion: Identified lead kinase inhibitors with strong and stable binding, providing promising candidates for further in vitro validation and structure-based drug design.

Objective: Investigate the binding interactions of antibiotics with bacterial DNA gyrase to identify potential inhibitors.

Key Points:

• Used AutoDock Vina to dock a library of fluoroquinolone antibiotics into the active site of DNA gyrase.

• Best docking pose showed a binding energy of -8.9 kcal/mol, forming hydrogen bonds with Asp426 and Ser531, key residues in DNA binding.

• Conducted Molecular Dynamics (MD) simulations using GROMACS to assess ligand stability and flexibility.

Conclusion: Results highlight potent inhibitors for further optimization and in vitro testing against resistant bacterial strains.

Objective: Analyze the molecular interactions between GPCRs and potential drug candidates using docking and MD simulations.

Key Points:

• Utilized AutoDock Vina to dock small molecules into the orthosteric binding site of a GPCR model.

• Top hit exhibited a binding energy of -10.1 kcal/mol, forming hydrogen bonds with Asp113 and Tyr185, critical for receptor activation.

• Performed Molecular Dynamics (MD) simulations using GROMACS to evaluate receptor-ligand stability and conformational changes.

Conclusion: Findings provide insights into GPCR-targeted drug design and candidate selection for experimental validation.

Objective: Screen a database of natural compounds for potential anticancer activity by targeting a key oncogenic protein.

Key Points:

• Conducted high-throughput virtual screening using AutoDock Vina against the active site of EGFR (Epidermal Growth Factor Receptor).

• Lead compound demonstrated a binding energy of -9.7 kcal/mol, forming hydrogen bonds with Met793 and Glu762.

• Followed up with Molecular Dynamics (MD) simulations using GROMACS to assess stability and ligand-induced conformational shifts.

Conclusion: Identified promising natural inhibitors for further evaluation in preclinical studies.

Objective: Explore the binding interactions and pharmacokinetics of potential anti-inflammatory compounds.

Key Points:

• Used AutoDock Vina to dock NSAIDs into the active site of COX-2 (Cyclooxygenase-2).

• Best compound had a binding energy of -8.5 kcal/mol, forming hydrogen bonds with Arg120 and Tyr355.

• Conducted ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis using SwissADME and pkCSM tools.

Conclusion: Selected compounds with strong binding affinity and favorable pharmacokinetics for further drug development.

Objective: Identify novel inhibitors targeting the main protease (Mpro) of SARS-CoV-2 using computational methods.

Key Points:

• Screened a library of antiviral compounds using AutoDock Vina to dock them into the catalytic site of Mpro.

• Best inhibitor displayed a binding energy of -9.2 kcal/mol, forming hydrogen bonds with Cys145 and His41.

• Conducted Molecular Dynamics (MD) simulations using GROMACS to assess binding stability over 100 ns.

Conclusion: Identified lead compounds for further in vitro and in vivo antiviral testing.

Objective: Use molecular docking and MM-PBSA calculations to estimate ligand binding affinity to a drug target.

Key Points:

• Performed AutoDock Vina docking on a series of inhibitors targeting a kinase active site.

• Selected the top three hits based on binding energy and interaction analysis.

• Carried out MM-PBSA (Molecular Mechanics Poisson–Boltzmann Surface Area) calculations in GROMACS to determine binding free energy.

Conclusion: Results provided a ranking of inhibitors based on thermodynamic stability, guiding future lead optimization efforts.