Discovering New Tyrosinase Inhibitors by Using In Silico Modelling, Molecular Docking, and Molecular Dynamics

Area of Science:

• Computational chemistry and cheminformatics
• Drug discovery and development
• Biochemistry and enzymology

Background:

• Tyrosinase is a key enzyme in melanin production, implicated in melanoma.
• In silico methods offer an efficient approach to screen for potential tyrosinase inhibitors.

Purpose of the Study:

• To identify novel tyrosinase inhibitors using computational modeling.
• To predict inhibitory activity (IC50) for a large dataset of chemical structures.
• To evaluate potential candidates for melanoma treatment.

Main Methods:

• Development and validation of quantitative structure-activity relationship (QSAR) models using machine learning algorithms.
• Screening of large databases of FDA-approved drugs and natural products.
• Molecular docking and molecular dynamics simulations for top candidates.
• ADME (Absorption, Distribution, Metabolism, and Excretion) analysis.

Main Results:

• A robust multiple linear regression model was developed with high statistical validation (R² = 0.8687, Q²LOO = 0.8030, Q²ext = 0.9151).
• Screening of 15,424 structures identified 15 potential tyrosinase inhibitors.
• Five top candidates with predicted high pIC50 values were selected for further analysis.
• Molecular docking and dynamics studies confirmed the inhibitory potential of the top five candidates.

Conclusions:

• The study successfully identified five novel chemical structures as potential tyrosinase inhibitors.
• These candidates show promise for therapeutic applications in melanoma treatment.
• In silico modeling provides a valuable strategy for accelerating drug discovery for tyrosinase-related conditions.