Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

Chemoinformatics-based classification of prohibited substances employed for doping in sport

Cannon, E. O. and Bender, A. and Palmer, D. S. and Mitchell, J. B. (2006) Chemoinformatics-based classification of prohibited substances employed for doping in sport. Journal of Chemical Information and Modeling, 46 (6). pp. 2369-2380.

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

Representative molecules from 10 classes of prohibited substances were taken from the World Anti-Doping Agency (WADA) list, augmented by molecules from corresponding activity classes found in the MDDR database. Together with some explicitly allowed compounds, these formed a set of 5245 molecules. Five types of fingerprints were calculated for these substances. The random forest classification method was used to predict membership of each prohibited class on the basis of each type of fingerprint, using 5-fold cross-validation. We also used a k-nearest neighbors (kNN) approach, which worked well for the smallest values of k. The most successful classifiers are based on Unity 2D fingerprints and give very similar Matthews correlation coefficients of 0.836 (kNN) and 0.829 (random forest). The kNN classifiers tend to give a higher recall of positives at the expense of lower precision. A naïve Bayesian classifier, however, lies much further toward the extreme of high recall and low precision. Our results suggest that it will be possible to produce a reliable and quantitative assignment of membership or otherwise of each class of prohibited substances. This should aid the fight against the use of bioactive novel compounds as doping agents, while also protecting athletes against unjust disqualification.

Item type: Article
ID code: 39172
Keywords: chemoinformatics , doping, sport, molecular biology, Physics, Microbiology
Subjects: Science > Physics
Science > Microbiology
Department: Faculty of Science > Physics
Faculty of Science > Mathematics and Statistics > Statistics and Modelling Science
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 16 Apr 2012 12:16
    Last modified: 04 Oct 2012 14:17
    URI: http://strathprints.strath.ac.uk/id/eprint/39172

    Actions (login required)

    View Item