Lexin Chen

The paper improves k-means clustering for MD simulations with new seeding strategies, reducing runtime without sacrificing clustering quality.

mdBIRCH clusters molecular dynamics trajectories efficiently using an online method with a RMSD-calibrated merge test, enabling scalable and interpretable analysis.

DIVINE provides a deterministic clustering framework for molecular dynamics trajectories, achieving better clustering quality and efficiency than bisecting k-means.

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.

The paper benchmarks undersampling techniques for imbalanced DNA-encoded library datasets using three machine learning models, finding that max_sim performs best and implementing the pipeline in DELight.

The paper introduces a new density-based clustering algorithm for molecular dynamics data to improve the identification of metastable states and rare events.

This paper introduces HELM, a hybrid clustering method combining k-means efficiency with hierarchical flexibility, and demonstrates its application to protein-DNA and protein folding simulations.

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

This paper improves Radial Threshold Clustering by reducing its time complexity to O(N) and making clustering results order-invariant.

The paper introduces BitBIRCH, a clustering algorithm for large molecular libraries that is faster and more memory-efficient than existing methods.

PRIME improves identification of native-like protein structures from MD simulations by using extended similarity indices, achieving lower RMSD than traditional centroid-based methods.

The paper introduces NANI, a deterministic k-means clustering algorithm for molecular dynamics simulations that improves cluster accuracy and reproducibility compared to k-means++.