The centroid approximation for mixtures: calculating similarity and deriving structure--activity relationships.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Drug Discovery

Background:

• Chemical compounds are frequently synthesized and evaluated as mixtures.
• Representing and analyzing mixtures poses computational challenges.

Purpose of the Study:

• To propose and validate a descriptor-based centroid approximation for chemical mixtures.
• To assess the utility of this approximation for similarity searching and structure-activity relationship (SAR) derivation.

Main Methods:

• Utilized atom pair and topological torsion descriptors.
• Simulated mixtures of druglike molecules from the MDL Drug Data Report database.
• Applied centroid approximation for mixture representation.

Main Results:

• Similarity searches with mixtures as queries or database entries produced reasonable outcomes.
• A correction was identified as necessary for mixture-mixture comparisons involving diverse molecules.
• Predictive SARs, in the form of trend vectors, were successfully derived from mixtures.

Conclusions:

• The centroid approximation offers a compact and computationally efficient method for representing chemical mixtures.
• This approach facilitates the application of existing cheminformatics tools to mixture analysis.
• The method shows promise for accelerating drug discovery through efficient SAR analysis of compound mixtures.