Institute of Pharmacology, Polish Academy of Sciences, Department of Medicinal Chemistry, Smętna Street 12, 31-343 Kraków, Poland.
Int J Mol Sci. 2018 Mar 30;19(4):1040. doi: 10.3390/ijms19041040.
Metabolic stability is an important parameter to be optimized during the complex process of designing new active compounds. Tuning this parameter with the simultaneous maintenance of a desired compound's activity is not an easy task due to the extreme complexity of metabolic pathways in living organisms. In this study, the platform for qualitative evaluation of metabolic stability, expressed as half-lifetime and clearance was developed. The platform is based on the application of machine learning methods and separate models for human, rat and mouse data were constructed. The compounds' evaluation is qualitative and two types of experiments can be performed-regression, which is when the compound is assigned to one of the metabolic stability classes (low, medium, high) on the basis of numerical value of the predicted half-lifetime, and classification, in which the molecule is directly assessed as low, medium or high stability. The results show that the models have good predictive power, with accuracy values over 0.7 for all cases, for Sequential Minimal Optimization (SMO), k-nearest neighbor (IBk) and Random Forest algorithms. Additionally, for each of the analyzed compounds, 10 of the most similar structures from the training set (in terms of Tanimoto metric similarity) are identified and made available for download as separate files for more detailed manual inspection. The predictive power of the models was confronted with the external dataset, containing metabolic stability assessment via the GUSAR software, leading to good consistency of results for SMOreg and Naïve Bayes (~0.8 on average). The tool is available online.
代谢稳定性是设计新活性化合物这一复杂过程中需要优化的一个重要参数。由于生物体内代谢途径的极端复杂性，要想在保持所需化合物活性的同时调整这一参数并非易事。在本研究中，建立了定性评估代谢稳定性的平台，以半衰期和清除率表示。该平台基于机器学习方法的应用，并构建了分别适用于人类、大鼠和小鼠数据的模型。化合物的评估是定性的，可以进行两种类型的实验——回归，即根据预测半衰期的数值将化合物分配到代谢稳定性类别（低、中、高）之一；分类，即直接评估分子的稳定性为低、中或高。结果表明，对于顺序最小优化（SMO）、k-最近邻（IBk）和随机森林算法，所有情况下模型的预测能力都很好，准确率均超过 0.7。此外，为分析的每种化合物，从训练集中识别出 10 种与 Tanimoto 度量相似度最相似的结构，并以单独的文件形式提供，以便更详细地手动检查。模型的预测能力与包含通过 GUSAR 软件进行的代谢稳定性评估的外部数据集进行了对比，结果显示 SMOreg 和朴素贝叶斯的结果一致性较好（平均约为 0.8）。该工具可在线使用。
