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

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 1. Introduction The problem of clustering can be defined as follows. The given N data points in a D -dimensional space should be organized into K clusters. Data points from one cluster should have more similarities than those from different clusters. Clustering algorithms can be classified as partition algorithms and hierarchical ones [1]. Partition algorithms are fast and require small memory. K-mean clustering is an example of a partition algorithm [2,3]. Hierarchical algorithms combine agglomerative and divisive algorithms. Generally, hierarchical algorithms are quite demonstrative. Agglomerative algorithms are

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