Improved 3D-QSAR prediction by multiple-conformational alignment: A case study on PTP1B inhibitors

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery

Background:

• Three-dimension quantitative structure-activity relationship (3D-QSAR) is crucial for correlating molecular structure with biological activity.
• Accurate 3D-QSAR modeling heavily relies on effective molecular alignment methodologies.
• Optimizing conformational search and superposition enhances predictive accuracy in QSAR.

Purpose of the Study:

• To optimize 3D-QSAR prediction accuracy using multiple conformational alignment methods.
• To evaluate docking-based alignment (DBA), pharmacophore-based alignment (PBA), and co-crystallized conformer-based alignment (CCBA) for PTP1B inhibitors.
• To identify the most effective alignment strategy for robust QSAR model development.

Main Methods:

• Developed and compared three distinct molecular alignment methods: DBA, PBA, and CCBA.
• Applied these methods to a dataset of 40 flexible PTP1B inhibitors.
• Utilized comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for model development.

Main Results:

• Co-crystallized conformer-based alignment (CCBA) emerged as the superior strategy, offering the best performance and speed.
• CCBA achieved high predictive accuracy with CoMFA (r²=0.992, q²=0.694) and CoMSIA (r²=0.972, q²=0.603).
• Generated robust QSAR models with informative molecular field contour maps.

Conclusions:

• CCBA is the most effective alignment method for developing accurate and efficient 3D-QSAR models.
• The developed QSAR models provide insights into essential structural features for novel PTP1B inhibitor design.
• This approach offers a general solution for constructing accurate 3D-QSAR models for various therapeutic targets.