Zaretzki Jed, Matlock Matthew, Swamidass S Joshua
Department of Pathology and Immunology, Washington University School of Medicine , St. Louis, Missouri 63130, United States.
J Chem Inf Model. 2013 Dec 23;53(12):3373-83. doi: 10.1021/ci400518g. Epub 2013 Nov 23.
Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.
了解外源性分子如何代谢很重要，因为这会影响药物的安全性、有效性和剂量，以及如何对它们进行修饰以改善这些特性。细胞色素P450（CYPs）是负责代谢市场上90%药物的蛋白质，许多计算方法可以预测分子的哪些原子位点——代谢位点（SOMs）——在CYP介导的代谢过程中会被修饰。本研究通过使用新的描述符和基于神经网络的机器学习改进了先前预测CYP介导的SOMs的方法。新方法XenoSite训练速度更快，对于某些同工酶，准确率提高了4%或5%。此外，XenoSite做出的一些“错误”预测随后通过对原始文献的重新评估被验证为正确预测。此外，XenoSite的输出可以解释为一种概率，它既反映了模型对特定原子被代谢的置信度，也反映了其对该原子预测正确的统计可能性。
