Improved understanding of aqueous solubility modeling through topological data analysis.

Area of Science:

• Computational chemistry
• Materials science
• Data science

Background:

• Topological data analysis (TDA) offers novel methods for understanding complex data structures.
• Chemical informatics relies on molecular descriptors to predict properties like solubility.
• Existing methods may not fully capture the intricate relationships within chemical descriptor spaces.

Purpose of the Study:

• To apply TDA techniques for visualizing and analyzing the chemical descriptor space related to solubility.
• To identify novel correlations between molecular features and solubility.
• To explore the utility of persistent homology on molecular graphs for property prediction.

Main Methods:

• Utilized the mapper algorithm, a TDA technique, for network visualization of the solubility descriptor space.
• Employed persistent homology on molecular graphs to create a parallel representation of chemical space.
• Applied norms to persistence landscapes for converting discrete to continuous shape descriptors.

Main Results:

• Generated a network visualization of the solubility space, highlighting chemically relevant descriptors.
• Discovered an unexpected correlation between chlorine content, the presence of rings, and solubility.
• Established links between the chemical space and descriptor space consistent with chemical heuristics.

Conclusions:

• TDA, specifically the mapper algorithm and persistent homology, provides powerful tools for exploring chemical data.
• The study reveals a previously unrecognized relationship between molecular topology (rings) and substituent effects (chlorine) on solubility.
• The developed methods offer a new perspective for quantitative structure-property relationship (QSPR) studies.