Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets

By Darko Butina. 1999

Subfields: Algorithms & Complexity, Computational Chemistry & Drug Discovery

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Citations in the corpus

Planning chemical syntheses with deep neural networks and symbolic AI (2017, methodology): “To obtain a set of target molecules that contain different scaffolds, first all molecules reported after 2014 were clustered using the extended connectivity fingerprint (ECFP6)-based Butina algorithm[46].”
Molecular de-novo design through deep reinforcement learning (2017, methodology): “…as the Tanimoto similarity, based on the RDKit implementation of binary Extended Connectivity Molecular Fingerprints with a diameter of 6 (ECFP6 [38]) was used as a similarity measure and the actives were clustered using the Butina clustering algorithm [39] in RDKit with a clustering cutoﬀ of 0.4.”
Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search (2023, methodology): “The compounds were clustered using the Taylor − Butina clustering algorithm (using a Tanimoto distance threshold of 0.3), 63 and the numbers of clusters containing only Fragment Network or similarity search compounds were counted.”
The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges (2022, background): “3) [60], and the number of clusters containing only Fragment Network or similarity search compounds were counted; the Fragment Network was found to result in more clusters across all targets (Supplementary Figure S6).”
CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome (2022, methodology): “We then clustered the 111,330 cavities with the Butina algorithm [34] (see Supplementary Methods).”
PubChem structure–activity relationship (SAR) clusters (2015, methodology): “This service uses a single-linkage hierarchical clustering algorithm [45], which is not the same as the Taylor–Butina algorithm [36, 37] used in this study.”
In silico fragmentation for computer assisted identification of metabolite mass spectra (2010, methodology): “Clusters of compounds having a structural Tanimoto similarity ≥ 0.95 are collapsed and treated as one compound cluster .”