Chemoinformatics-based classification of prohibited substances employed for doping in sport
Chemoinformatics-based classification of prohibited substances employed for doping in sport
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DOI：
10.1021/ci0601160
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发表时间：
2006-11-27
影响因子：
5.6
通讯作者：
Mitchell, John B. O.
中科院分区：
文献类型：
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作者：
Cannon, Edward O.;Bender, Andreas;Mitchell, John B. O.
从世界反兴奋剂机构（WADA）的清单中提取了10类违禁物质的代表性分子，并从MDDR数据库中找到了相应活性类别的分子。与一些明确允许的化合物一起，它们形成了一组5245个分子。对这些物质计算了五种类型的指纹。采用随机森林分类方法，通过5次交叉验证，在每种指纹类型的基础上预测每个禁止类的隶属度。我们还使用了k最近邻（kNN）方法，该方法对于k的最小值效果很好。最成功的分类器是基于Unity 2D指纹，并给出非常相似的马修斯相关系数0.836 （kNN）和0.829（随机森林）。kNN分类器倾向于以较低的精度为代价给出较高的正面召回率。然而，朴素贝叶斯分类器更倾向于高召回率和低准确率的极端。我们的研究结果表明，将有可能对每一类禁用物质的成员资格或其他方面进行可靠和定量的分配。这将有助于打击使用具有生物活性的新型化合物作为兴奋剂，同时也保护运动员免受不公正的取消资格。
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 naive 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.