Integrative Computational Approaches for TRPV1 Ion Channel Inhibitor Discovery: An Integrated Machine Learning, Drug...

Area of Science:

• Computational chemistry
• Pharmacology
• Molecular biology

Background:

• The Transient Receptor Potential Vanilloid 1 (TRPV1) ion channel is a key target for pain and inflammation management.
• Developing novel TRPV1 modulators is crucial for new analgesic therapies.
• Efficient drug design protocols are needed to explore extensive chemical spaces.

Purpose of the Study:

• To identify potential TRPV1 modulators from FDA-approved drugs using a computational framework.
• To evaluate the binding affinity and stability of identified drug candidates.

Main Methods:

• Machine learning (ML) models trained on ChEMBL bioactivity data.
• Virtual screening of FDA-approved drugs.
• Ensemble molecular docking on multiple TRPV1 cryo-EM structures.
• Molecular dynamics (MD) simulations and MM-GBSA calculations.

Main Results:

• 670 potential TRPV1 modulators were identified from FDA-approved drugs using ML.
• CYM-5442, Rociletinib, and SC-51089 showed strong binding affinities and stability.
• These candidates outperformed known TRPV1 modulators like Capsazepine and Capsaicin.

Conclusions:

• The combined ML and molecular simulation approach is effective for drug discovery.
• Identified compounds serve as promising starting points for developing next-generation TRPV1 analgesics.
• This study provides valuable insights for future experimental validation and optimization.