Classifying the world anti-doping agency's 2005 prohibited list using the Chemistry Development Kit fingerprint

Edward O. Cannon, John Blayney Owen Mitchell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


We used the freely available Chemistry Development Kit (CDK) fingerprint to classify 5235 representative molecules taken from ten banned classes in the 2005 World Anti-Doping Agency's (WADA) prohibited list, including molecules taken from the corresponding activity classes in the MDL Drug Data Report (MDDR). We used both Random Forest and k-Nearest Neighbours (kNN) algorithms to generate classifiers. The kNN classifiers with k = 1 gave a very slightly better Matthews Correlation Coefficient than the Random Forest classifiers; the latter, however, predicted fewer false positives. The performance of kNN classifiers tended to decline with increasing k. The performance of the CDK fingerprint is essentially equivalent to that of Unity 2D. Our results suggest that it will be possible to use freely available chemoinformatics tools to aid the fight against drugs in sport, while minimising the risk of wrongfully penalising innocent athletes.

Original languageEnglish
Title of host publicationComputational Life Sciences II
Subtitle of host publicationSecond International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006, Proceedings
EditorsM.R. Berthold, R. Glen, I. Fischer
Number of pages10
ISBN (Print)978-3-540-45767-1
Publication statusPublished - 2006
Event2nd International Symposium on Computational Life Sciences - Cambridge, United Kingdom
Duration: 27 Sept 200629 Sept 2006

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference2nd International Symposium on Computational Life Sciences
Country/TerritoryUnited Kingdom


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