Algorithms 2008, 1(2), 183-200; doi:10.3390/a1020183
Article

Hierarchical Clustering of Large Databases and Classification of Antibiotics at High Noise Levels

Institute of Physiologically Active Compounds, Russian Academy of Sciences, 142432, Chernogolovka, Moscow Region, Russia
* Author to whom correspondence should be addressed.
Received: 23 October 2008; in revised form: 23 November 2008 / Accepted: 5 December 2008 / Published: 18 December 2008
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
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Abstract: A new algorithm for divisive hierarchical clustering of chemical compounds based on 2D structural fragments is suggested. The algorithm is deterministic, and given a random ordering of the input, will always give the same clustering and can process a database up to 2 million records on a standard PC. The algorithm was used for classification of 1,183 antibiotics mixed with 999,994 random chemical structures. Similarity threshold, at which best separation of active and non active compounds took place, was estimated as 0.6. 85.7% of the antibiotics were successfully classified at this threshold with 0.4% of inaccurate compounds. A .sdf file was created with the probe molecules for clustering of external databases.
Keywords: Molecular structure; hierarchical clustering; algorithm; classification of antibiotics

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MDPI and ACS Style

Trepalin, S.V.; Yarkov, A.V. Hierarchical Clustering of Large Databases and Classification of Antibiotics at High Noise Levels. Algorithms 2008, 1, 183-200.

AMA Style

Trepalin SV, Yarkov AV. Hierarchical Clustering of Large Databases and Classification of Antibiotics at High Noise Levels. Algorithms. 2008; 1(2):183-200.

Chicago/Turabian Style

Trepalin, Sergei V.; Yarkov, Alexander V. 2008. "Hierarchical Clustering of Large Databases and Classification of Antibiotics at High Noise Levels." Algorithms 1, no. 2: 183-200.

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