MetaTREE,a Novel Database Focused on Metabolic Trees,Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.     

基于MOLMAP 指数的光化学反应分类预测

由反应物和产物的结构衍生了反应物、产物和化学反应的MOLMAP 指数, 其中化合物的结构由化学键的物理化学性质和拓扑性质所表征. 将前述MOLMAP 指数应用于一个含七类光化学反应的数据集, 通过随机森林建立了三种类型的模型: (1)预测反应物可能发生的反应类型; (2)预测可能合成产物的反应类型; (3)预测整个化学反应的类型. 由于无需指定数据集中参与反应的化学键, 所以, MOLMAP 指数能够得到广泛的应用. 所得分类预测结果好于我们此前对同一数据集的研究, 表明改进化学键的描述有助于提高MOLMAP 指数的预测能力.     

Organic Stereochemistry. Part 8

This review terminates our general presentation of the principles of stereochemistry with special reference to the biomedicinal sciences. Here, we discuss and illustrate the principles of prostereoisomerism, and apply these to product and substrate? product stereoselectivity in drug metabolism. The review begins with an overview of the concept of prostereoisomerism, discussing such aspects as homotopic, enantiotopic, and diastereotopic groups and faces. The main part of this review is dedicated to drug and xenobiotic metabolism. Here, the concept of prostereoisomerism proves particularly helpful to avoid confusing metabolic reactions in which an existing stereogenic element (e.g., a stereogenic center) influences the course of the reaction (substrate stereoselectivity), with metabolic reactions which create a stereogenic element (almost always a stereogenic center; product stereoselectivity). Specifically, examples of product stereoselectivity will be taken from functionalization reactions (so‐called phase‐I reactions) and conjugation (so‐called phase‐II reactions). Cases where stereoisomeric substrates show distinct product stereoselectivities (substrate? product stereoselectivity) will also be presented.     

支持向量机与KStar模型预测细胞色素P450酶催化的氧脱烃反应

分别以支持向量机(SVM)和KStar方法为基础, 构建了代谢产物的分子形状判别和代谢反应位点判别的嵌套预测模型. 分子形状判别模型是以272个分子为研究对象, 计算了包括分子拓扑、二维自相关、几何结构等在内的1280个分子描述符, 考查了支持向量机、决策树、贝叶斯网络、k最近邻这四种机器学习方法建立分类预测模型的准确性. 结果表明, 支持向量机优于其他方法, 此模型可用于预测分子能否被细胞色素P450酶催化发生氧脱烃反应. 代谢反应位点判别模型以538个氧脱烃反应代谢位点为研究对象, 计算了表征原子能量、价态、电荷等26个量子化学特征, 比较了决策树、贝叶斯网络、KStar、人工神经网络建模的准确率. 结果显示, KStar模型的准确率、敏感性、专一性均在90%以上, 对分子形状判别模型筛选出的分子, 此模型能较好地判断出哪个C―O键发生断裂. 本文以15个代谢反应明确的中药分子为验证集, 验证模型准确性, 研究结果表明基于SVM和KStar的嵌套预测模型具有一定的准确性, 有助于开展中药分子氧脱烃代谢产物的预测研究.