FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity
FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity
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DOI：
10.1021/acs.jcim.7b00250
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发表时间：
2017-08-01
影响因子：
5.6
通讯作者：
Kirchmair, Johannes
中科院分区：
文献类型：
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作者：
Sicho, Martin;Kops, Christina de Bruyn;Kirchmair, Johannes
我们报道了FASt MEtabolizer（FAME; J. Chem. Inf. Model. 2013，53，2896 - 2907），用于预测异生物质代谢位点（SoM）的随机森林模型的集合。探索了一组广泛的描述符，从简单的2D描述符（如FAME中使用的描述符）到目前可用的一些最准确的SoM预测模型中使用的量子化学描述符。与最初的FAME方法一致，我们的目标是保持简单，并提出基于少量2D描述符的准确和鲁棒的模型。我们发现，用这种描述符对原子及其环境进行循环描述，再结合一种极其随机的树算法，可以产生与更复杂的方法相比表现同样出色的模型。在独立测试集上进行的全面评估实验表明，这些模型中最好的模型获得了马修斯相关系数、受试者工作特征曲线下面积和Top-2准确度，分别为0.57、0.91和94.1%。还开发了用于预测β 3A4、2D6和2C9的异构体特异性区域选择性的模型，并显示出竞争性性能。最好的模型已被集成到一个新开发的软件包（FAME 2），该软件包可从作者那里免费获得。
We report on the further development of FAst MEtabolizer (FAME; J. Chem. Inf. Model. 2013, 53, 2896-2907), a collection of random forest models for the prediction of sites of metabolism (SoMs) of xenobiotics. A broad set of descriptors was explored, from simple 2D descriptors such as those used in FAME, to quantum chemical descriptors employed in some of the most accurate models for SoM prediction currently available. In line with the original FAME approach, our objective was to keep things simple and to come up with accurate and robust models that are based on a small number of 2D descriptors. We found that circular descriptions of atoms and their environments with such descriptors in combination with an extremely randomized trees algorithm can yield models that perform equally well compared to more complex approaches. Thorough evaluation experiments on an independent test set showed that the best of these models obtained a Matthews correlation coefficient, area under the receiver operating characteristic curve, and Top-2 accuracy of 0.57, 0.91 and 94.1%, respectively. Models for the prediction of isoform-specific regioselectivity of CYP 3A4, 2D6, and 2C9 were also developed and showed competitive performance. The best models have been integrated into a newly developed software package (FAME 2), which is available free of charge from the authors.