Comparative Exploration on Quantifying Molecular Diversity.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Analytical Chemistry

Background:

• Molecular diversity is crucial for structure-property relationships and predictive modeling.
• Effective molecular representations and similarity measures are needed for accurate diversity quantification.
• Existing research has gaps in understanding feature-property links, binarization impacts, and feature space effects on diversity.

Purpose of the Study:

• To address limitations in molecular similarity and diversity assessment.
• To explore the impact of various molecular featurization techniques and similarity measures.
• To introduce and evaluate a novel cumulative binarization approach.

Main Methods:

• Exploration of structure-based descriptors (E-state, Morgan fingerprints, ABOCH) and physicochemical properties (Abraham solute descriptors, retention factors).
• Assessment of different similarity measures, including distance metrics and binarization techniques.
• Introduction of a cumulative binarization approach for enhanced structural information preservation.
• Comparative analysis using datasets of compounds with experimentally measured retention factors.

Main Results:

• Key differences identified between structural and physicochemical molecular representations.
• Demonstrated impact of feature spaces and similarity measures on molecular similarity rankings and diversity quantification.
• Validation of the novel cumulative binarization approach's effectiveness.

Conclusions:

• Molecular feature space selection significantly influences similarity and diversity assessments.
• Findings have direct implications for optimizing chemical processes and improving predictive modeling in analytical chemistry and molecular modeling.
• The study provides a more robust framework for quantifying molecular diversity.