Target Discovery Using Deep Learning-Based Molecular Docking and Predicted Protein Structures With AlphaFold for...

Area of Science:

• Neuropharmacology
• Computational Chemistry
• Drug Discovery

Background:

• Schizophrenia treatment-resistant cases require novel therapeutic agents.
• Atypical antipsychotics primarily target 5-HT2A and dopaminergic receptors, necessitating exploration of additional receptor targets.

Purpose of the Study:

• To investigate novel receptor binding affinities of existing antipsychotics.
• To identify potential new drug candidates for antipsychotic-resistant schizophrenia using computational methods.

Main Methods:

• Utilized GNINA (Deep Learning Based Molecular Docking) and AlphaFold (Predicted Protein Structures) to assess binding affinities.
• Evaluated binding affinities between clozapine, olanzapine, and quetiapine with various neuropharmacological, immunological, and metabolic receptors.

Main Results:

• Clozapine, olanzapine, and quetiapine demonstrated high binding affinities to receptors including CB2, 5-HT1BR, NPYR4, and CCR5.
• Identified cyclosporin A and everolimus as potential candidates for new antipsychotic drug development due to their high affinities with these target receptors.

Conclusions:

• The computational methodology employed can guide the development of novel antipsychotic drugs.
• Future applications include drug repositioning and elucidating schizophrenia pathophysiology.