Volume 4 Supplement 1

7th German Conference on Chemoinformatics: 25 CIC-Workshop

Open Access

Probabilistic classifier: generated using randomised sub-sampling of the feature space

Journal of Cheminformatics20124(Suppl 1):P40

https://doi.org/10.1186/1758-2946-4-S1-P40

Published: 1 May 2012

Nowadays supervised classification, based on the concept of pattern recognition, is an integral part of virtual screening. The central idea of supervised classification in chemoinformatics is to design a classifying algorithm that accurately assigns a new molecule to one of a set of predefined classes.

Naturally, probabilistic classifiers can be far more useful than hard point classifiers in making a decision on problems [1], such as virtual screening, where there is an associated risk in classifying an instance to one class or the other.

For their conceptual simplicity and computational efficiency probabilistic classification methods based on the Naive Bayes concept are widely employed in chemoinformatics. The simplicity of the Naive Bayes is due to the assumption that the descriptors representing the molecule one desires to classify are statistically independent. Unfortunately it is well documented that when the molecular descriptors are binary-valued - which is often the case in chemoinformatics - and thus take values of 0 or 1 the Naive Bayesian classifier can only act as a linear classifier in the descriptor space.

Techniques such as the Parzen-Window approach can address the above shortcomings but suffer from being computationally expensive as they require one to retain all the training dataset in core memory [2, 3].

In an attempt to address the above mentioned drawbacks, a new probabilistic classifier is proposed which uses randomized sub-sampling of the descriptor space. The proposed algorithm generates better class membership predictions than its Naive Bayesian counterpart on classifying molecules that are non-linearly separable in descriptor space.

We present a realistic test of the new method by classifying large chemical datasets generated from the ChEMBL database [4].

Authors’ Affiliations

(1)
Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge

References

  1. Duda RO, Hart PE: Pattern Classification and Scene Analysis. 1973, John Wiley & Sons, Ltd : New York, NYGoogle Scholar
  2. Parzen E: The Annals of Mathematical Statistics. 1962, 33: 1065-1076.Google Scholar
  3. Harper G, Bradshaw J, Gittins JC, Green DVS, Leach AR: . J Chem Inf Comput Sci. 2001, 41: 1295-1300. 10.1021/ci000397q.View ArticleGoogle Scholar
  4. ChEMBL. J Comput-Aided Mol Des. 2009, 4: 195-198.Google Scholar

Copyright

© Tyzack et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.