The search for functional correspondences in molecular structure between two dissimilar molecules.

Area of Science:

• Computational chemistry
• Molecular modeling

Background:

• Competitive binding is crucial in pharmacology and toxicology.
• Identifying structural similarities between ligands can predict binding interactions.
• Quantifying these similarities requires robust geometric analysis.

Purpose of the Study:

• To develop and test an algorithm for finding optimal structural correspondences between molecular ligands.
• To enable precise geometric fitting for identifying potential competitive binding relationships.

Main Methods:

• An algorithm employing a tree-search technique to explore all possible sets of ligand point correspondences.
• Incorporation of parameters for the number of correspondences and fit tolerance.
• Tree pruning methods to optimize search efficiency.

Main Results:

• The algorithm successfully identifies the set of correspondences yielding the best geometric fit between ligand points.
• Demonstrated efficacy using the marine neurotoxins saxitoxin and tetrodotoxin as test cases.

Conclusions:

• The developed algorithm provides an effective method for quantifying structural similarities between ligands.
• This approach has implications for understanding and predicting competitive binding in molecular interactions.