Krisztina Zsigmond

This study examines how undersampling techniques affect hyperparameter selection for non-linear dimensionality reduction methods in chemical space visualization, showing reduced computational costs and improved local structure preservation.

BitBIRCH-Lean efficiently clusters large molecular libraries on workstations, achieving 2X speedup over the original BitBIRCH with no loss in cluster quality.

The paper introduces a method to visualize chemical space by projecting molecular data onto a lower-dimensional space using linear methods, outperforming non-linear techniques in efficiency and neighborhood preservation.

SHINE improves pathway ensemble analysis by integrating n-ary similarity, cheminformatics tools, and hierarchical clustering for MD simulations.

The paper demonstrates how to generate a metric consistent with Tanimoto similarity and explores its properties and relationships with other similarity measures.