Bayesian methods for the conformational classification of eight-membered rings

Area of Science:

• Computational Chemistry
• Structural Chemistry
• Cheminformatics

Background:

• Eight-membered rings (cyclooctanes) exhibit complex conformational flexibility.
• Understanding these conformations is crucial in various chemical disciplines.
• Existing classification methods may not capture all possible conformations.

Purpose of the Study:

• To develop and present two novel Bayesian analysis methods for classifying eight-membered ring conformations.
• To compare the performance of these methods against literature data and experimental databases.
• To enable the detection of previously unknown ring conformations.

Main Methods:

• Development of two Bayesian classification methods utilizing a probabilistic model for torsion angle measurement.
• Method 1: Classification based on known canonical forms of cyclooctane.
• Method 2: Unsupervised clustering for data-driven classification and detection of novel conformations.

Main Results:

• Both methods were successfully applied to classify Csp3 eight-membered rings using literature data.
• Classification of solid-state conformations in Csp3 eight-membered rings was performed using Cambridge Structural Database (CSD) data.
• The second method demonstrated the ability to identify new conformational clusters beyond known canonical forms.

Conclusions:

• The presented Bayesian methods offer robust approaches for classifying eight-membered ring conformations.
• These methods enhance the understanding of cyclooctane conformational space, including the discovery of new forms.
• The approach is applicable to both theoretical and experimentally derived conformational data.