Topomer CoMFA: a design methodology for rapid lead optimization.

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Quantitative Structure-Activity Relationship (QSAR) methodologies are crucial for accelerating drug lead optimization.
• Existing methods can be computationally intensive and may not fully leverage fragment-based approaches.

Purpose of the Study:

• To develop an objective QSAR methodology by merging CoMFA and topomer technologies.
• To enhance the efficiency and predictive power of lead optimization in drug discovery.

Main Methods:

• A novel
• topomer CoMFA
• method was developed by fragmenting input structures and generating 3D models.
• Steric and electrostatic fields were calculated for each fragment set.
• The method was applied to 15 literature-based 3D-QSAR analyses.

Main Results:

• The
• topomer CoMFA
• method demonstrated successful application across 847 structures from literature.
• Achieved an average q(2) of 0.520 and an average SDEP of 0.688 for 133 predicted structures.
• Successfully identified commercially available fragments predicted to increase molecular potency by an average of 20x.

Conclusions:

• The merged
• topomer CoMFA
• approach offers a promising strategy for accelerating lead optimization.
• This methodology facilitates efficient virtual screening of fragment libraries for enhanced potency.
• Demonstrates a practical pathway for in silico drug optimization.