Metric and multidimensional scaling: efficient tools for clustering molecular conformations

Area of Science:

• Computational chemistry
• Cheminformatics
• Data analysis

Background:

• Conformer ensembles are crucial for understanding molecular behavior.
• Efficient methods are needed for analyzing and clustering these ensembles.
• Existing clustering techniques may lack speed or intuitive visualization.

Purpose of the Study:

• To demonstrate the application of metric and multidimensional scaling for conformer ensemble analysis.
• To develop an automated process for clustering and assigning group memberships.
• To compare the effectiveness of different scaling and clustering algorithms.

Main Methods:

• Application of metric scaling and multidimensional scaling (MDS) to conformer ensembles.
• Development of an automated pipeline for clustering and identifying representative conformers.
• Evaluation of hierarchical clustering algorithms using multidimensional plots.

Main Results:

• Multidimensional scaling outperformed metric scaling in clustering conformers.
• An automated clustering process yielded rapid and visually interpretable results.
• The group average hierarchical clustering algorithm demonstrated the best performance.

Conclusions:

• MDS is a powerful tool for analyzing conformer ensembles.
• Automated clustering with MDS enables efficient and intuitive data exploration.
• The group average method is recommended for hierarchical clustering of conformers.