NDLI: Novel approaches for small biomolecule classification and structural similarity search

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Volume 9, Issue 2, December 2007

Volume 9, Issue 1, June 2007

Introduction to the special issue on data mining for health informatics

Interview with Simon Funk

Unsupervised pattern mining from symbolic temporal data

Enabling more sophisticated gene expression analysis for understanding diseases and optimizing treatments

5th international workshop on knowledge discovery in inductive databases (KDID'06)

Novel approaches for small biomolecule classification and structural similarity search

Drug exposure side effects from mining pregnancy data

Automatic in vivo microscopy video mining for leukocytes

Novel approaches for small biomolecule classification and structural similarity search

Content Provider	ACM Digital Library
Author	Cherkasov, Artem Sahinalp, S. Cenk Karakoc, Emre
Abstract	Structural similarity search among small molecules is a standard tool used in molecular classification and in-silico drug discovery. The effectiveness of this general approach depends on how well the following problems are addressed. The notion of similarity should be chosen for providing the highest level of discrimination of compounds with respect to the bioactivity of interest. The data structure for performing search should be very efficient as the molecular databases of interest include several millions of compounds. In this paper we summarize the recent applications of k-nearest-neighbor search method for small molecule classification. The k-nn classification of small molecules is based on selecting the most relevant set of chemical descriptors which are then compared under standard Minkowski distance $L_{p}.$ Here we describe how to computationally design the optimal weighted Minkowski distance $wL_{p}$ for maximizing the discrimination between active and inactive compounds wrt bioactivities of interest. k-nn classification requires fast similarity search for predicting bioactivity of a new molecule. We then focus on construction of pruning based k-nn search data structures for any $wL_{p}$ distance that minimizes similarity search time. The accuracy achieved by k-nn classifier is better than the alternative LDA and MLR approaches and is comparable to the ANN methods. In terms of running time, k-nn classifier is considerably faster than the ANN approach especially when large data sets are used. Furthermore, k-nn classifier is capable of quantification of the level of bioactivity rather than returning a binary decision and can bring more insight to the nature of the activity via eliminating unrelated descriptors of the compounds with respect to the activity in question.
Starting Page	14
Ending Page	21
Page Count	8
File Format	PDF
DOI	10.1145/1294301.1294307
Journal	ACM SIGKDD Explorations Newsletter (SKDD)
Volume Number	9
Issue Number	1
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-06-01
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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