In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular...

Area of Science:

• Computational chemistry
• Drug discovery
• Biochemistry

Background:

• Fyn kinase is a crucial regulator in various cellular processes.
• Dysregulation of Fyn kinase is implicated in diseases like cancer.
• Targeting Fyn kinase presents a therapeutic opportunity.

Purpose of the Study:

• To identify novel Fyn kinase inhibitors using a multi-computational approach.
• To develop and validate a predictive QSAR model for Fyn kinase activity.
• To assess the drug-likeness and binding stability of potential inhibitors.

Main Methods:

• Quantitative Structure-Activity Relationship (QSAR) model development using XGBoost.
• Molecular docking simulations to predict binding affinity and interactions.
• Absorption, Distribution, Metabolism, and Excretion (ADME) profiling.
• Molecular dynamics (MD) simulations for stability assessment.

Main Results:

• A QSAR model with 0.95 accuracy was developed for Fyn kinase inhibition.
• Nine compounds were identified as potential Fyn kinase inhibitors.
• Four compounds (728, 734, 736, 852) demonstrated stable binding with Fyn kinase in MD simulations.
• A web-based platform was created for streamlined Fyn kinase inhibitor screening.

Conclusions:

• The integrated computational strategy effectively identifies potential Fyn kinase inhibitors.
• Compounds 728, 734, 736, and 852 are promising candidates for further investigation.
• The developed QSAR model and screening platform can accelerate drug discovery efforts for Fyn kinase.