Hierarchical analysis of the target-based scoring function modification for the example of selected class A GPCRs

Area of Science:

• Computational chemistry
• Pharmacology
• Structural biology

Background:

• Molecular docking is crucial for drug discovery, but scoring functions (SFs) often yield suboptimal results.
• Accurate prediction of ligand-protein interactions is essential for identifying new bioactive agents.

Purpose of the Study:

• To investigate the impact of re-estimating scoring function (SF) weights on molecular docking accuracy for class A G-protein coupled receptors (GPCRs).
• To evaluate whether a tailored, individual approach to SF definition improves binding affinity prediction and active compound recognition compared to general schemes.

Main Methods:

• Docking calculations were performed on class A GPCRs using known ligands and affinity data from the ChEMBL database.
• Scoring functions were re-weighted at different biological hierarchy levels: the entire class, sub-subfamilies, and individual proteins.
• Performance was assessed by evaluating binding affinity prediction and active compound recognition accuracy.

Main Results:

• A significant improvement in molecular docking results was observed with the re-weighted, designed SF definitions.
• The individual protein-level approach demonstrated the most substantial enhancement in accuracy.
• Tailored SFs improved both binding affinity prediction and the recognition of active compounds.

Conclusions:

• Individualized scoring function definitions for specific proteins significantly enhance molecular docking performance.
• This tailored strategy increases the efficiency and predictive power of virtual screening in drug discovery.
• Considering the biological hierarchy for SF design is vital for optimizing computational drug discovery workflows.