Abstract
Clustering of molecular systems according to their three-dimensional structure is animportant step in many bioinformatics workflows. In applications such as docking or structure prediction, many algorithms initially generate large numbers of candidate poses (or decoys), which are then clustered to allow for subsequent computationally expensive evaluations of reasonable representatives. Since the number of such candidates can easily range from thousands to millions, performing the clustering on standard central processing units …
Citation
[HSH+15] Hoang-Vu, D., Schmidt, B., Hildebrandt, A., Tran, T.T. and Hildebrandt, A.-K: CUDA-enabled hierarchical ward clustering of protein structures based on the nearest neighbour chain algorithm International Journal of High Performance Computing Applications, 2015