Hologram quantitative structure activity relationship studies on 5-HT6 antagonists

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• 5-HT6 receptor antagonists are investigated for potential therapeutic applications.
• Arylsulfonamide compounds have shown promise as 5-HT6 antagonists.
• Predictive modeling is crucial for optimizing drug candidates.

Purpose of the Study:

• To develop predictive hologram quantitative structure-activity relationship (HQSAR) models for arylsulfonamide 5-HT6 antagonists.
• To validate the predictive performance of the developed HQSAR models.
• To elucidate atomic contributions to compound activity using HQSAR.

Main Methods:

• Development of HQSAR models using training sets of arylsulfonamide compounds.
• Fragment distinction based on atoms, bond, and connectivity with fragment size 4-7.
• Validation of models using external test sets.

Main Results:

• The best HQSAR model for specific antagonists achieved q2=0.702 and r2=0.971, with predictive r2=0.678 on an external set.
• A second HQSAR model for diverse antagonists showed q2=0.693 and r2=0.923, with predictive r2=0.692 on an external set.
• Contribution maps identified key atomic influences on 5-HT6 antagonist activity.

Conclusions:

• HQSAR is an effective method for predicting the activity of 5-HT6 antagonists.
• The developed models provide insights into structure-activity relationships for arylsulfonamides.
• This approach aids in the rational design of novel 5-HT6 antagonist drug candidates.