title: 3-D clustering: a tool for high throughput docking.
abstract: This report describes a computer program for clustering docking poses based on their 3-dimensional (3D) coordinates as well as on their chemical structures. This is chiefly intended for reducing a set of hits coming from high throughput docking, since the capacity to prepare and biologically test such molecules is generally far more limited than the capacity to generate such hits. The advantage of clustering molecules based on 3D, rather than 2D, criteria is that small variations on a scaffold may bring about different binding modes for molecules that would not be predicted by 2D similarity alone. The program does a pose-by-pose/atom-by-atom comparison of a set of docking hits (poses), scoring both spatial and chemical similarity. Using these pair-wise similarities, the whole set is clustered based on a user-supplied similarity threshold. An output coordinate file is created that mirrors the input coordinate file, but contains two new properties: a cluster number and similarity to the cluster center. Poses in this output file can easily be sorted by cluster and displayed together for visual inspection with any standard molecular viewing program, and decisions made about which molecule should be selected for biological testing as the best representative of this group of similar molecules with similar binding modes.
authors.affiliation: Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research, 4002 Basel, Switzerland. john.priestle@novartis.com
authors.forename: John P
authors.initials: JP
authors.lastname: Priestle
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D012984:Software; D013997:Time Factors