Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures

Area of Science:

• Computational Chemistry
• Cheminformatics
• Quantitative Structure-Property Relationship (QSPR) Modeling

Background:

• Molecular structure encoding is crucial for predictive modeling.
• Existing methods may not efficiently capture complex chemical information.
• Quantitative Structure-Property Relationship (QSPR) models require robust descriptors.

Purpose of the Study:

• To propose substructural fragments as a method for encoding molecular structures and knowledge.
• To demonstrate the utility of fragments in QSPR modeling for diverse chemical systems.
• To explore the application of Condensed Graphs of Reactions (CGR) for chemical reaction analysis.

Main Methods:

• Encoding molecular structures using substructural fragments in occurrence matrices.
• Integrating knowledge from QSPR modeling into descriptor matrices.
• Representing complex supramolecular systems and chemical reactions (CGR).
• Applying fragment-based descriptors in QSPR studies for solubility and hydrogen-bonding thermodynamics.

Main Results:

• Fragments effectively represent molecular structures and knowledge for QSPR.
• Demonstrated efficiency in predicting aqueous solubility for organic compounds.
• Successfully analyzed thermodynamic parameters for hydrogen-bonding in supramolecular complexes.
• Condensed Graphs of Reactions (CGR) show potential for chemical reaction similarity searches.

Conclusions:

• Substructural fragments provide a versatile and efficient descriptor for QSPR.
• The matrix-based approach is applicable to diverse chemical entities, including reactions.
• Further investigation into descriptor matrix density and QSPR model robustness is warranted.