Refined docking as a valuable tool for lead optimization: application to histamine H3 receptor antagonists

Area of Science:

• Computational chemistry
• Drug discovery
• Pharmacology

Background:

• Structure-based drug design is crucial for virtual screening in drug discovery.
• Lead optimization, a complex but vital stage, is less explored computationally.
• Accurate prediction of ligand affinity and drug safety is essential.

Purpose of the Study:

• To develop and validate a homology model for the histamine H(3) receptor.
• To assess the model's ability to discriminate ligands with high affinity.
• To evaluate drug safety by studying antitargets like HERG and CYP2D6.

Main Methods:

• Homology modeling of the histamine H(3) receptor.
• Ligand docking and virtual screening.
• Benchmarking using Area Under the Receiver Operating Characteristic Curve (AUC of ROC) plots.
• Testing with extensive chemical libraries and Structure-Activity Relationship (SAR) data.

Main Results:

• The homology model demonstrated over 70% accuracy in classifying ligands above a defined affinity threshold for both the histamine H(3) receptor and antitargets.
• The stringent testing conditions, including large datasets and consideration of false positives/negatives, validated the model's performance.
• The method proved effective in predicting ligand affinity and safety profiles.

Conclusions:

• Homology modeling combined with docking provides a robust and accurate method for hit/lead optimization in drug discovery.
• This approach significantly enhances the efficiency of identifying potential drug candidates and assessing their safety.
• The validated procedure offers a reliable tool for medicinal chemists in complex optimization tasks.