Metabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we investigated the possibility of creating SAR models for the prediction of the qualitative metabolite yield (‘major’, ‘minor’, ”trace” and ”negligible”) depending on species and biological experimental systems. In addition, we have created models for prediction of xenobiotic excretion depending on its administration route for different species. The prediction is based on an algorithm of naïve Bayes classifier implemented in PASS software. The average accuracy of prediction was 0.91 for qualitative metabolite yield prediction and 0.89 for prediction of xenobiotic excretion. The created models were included as a component of MetaTox web application, which allows predicting the xenobiotic metabolism pathways (http://www.way2drug.com/mg).

代谢物鉴定是药物发现和开发过程的重要组成部分。实验方法可以鉴定代谢物并估计其相对量，但是它们需要成本密集且耗时的技术。代谢物预测的计算方法没有这些缺点，可以在药物发现的早期应用。在这项研究中，我们调查了建立SAR模型以预测定性代谢物产量（“主要”，“次要”，“痕量”和“微不足道”）的可能性，具体取决于物种和生物学实验系统。此外，我们已经根据异种的给药途径建立了预测异种生物排泄的模型。该预测基于在PASS软件中实现的朴素贝叶斯分类器算法。定性代谢物产量预测的平均预测准确度为0.91，异源生物排泄的预测平均准确度为0.89。所创建的模型作为MetaTox Web应用程序的组成部分包含在内，该应用程序可以预测异源代谢途径（http://www.way2drug.com/mg）。