Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network.
Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network.
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DOI：
10.1021/acscentsci.5b00131
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发表时间：
2015-07-22
影响因子：
18.2
通讯作者：
Swamidass SJ
中科院分区：
文献类型：
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作者：
Hughes TB;Miller GP;Swamidass SJ
药物毒性通常是由与蛋白质共价结合的亲电子反应性代谢物引起的。环氧化物包含一大类三元环醚。这些分子是亲电子的，并且由于环张力和极化碳氧键而通常具有高反应性。环氧化物是通常由细胞色素 P450 作用于芳香键或双键形成的代谢物。分子上进行环氧化的特定位置是其环氧化位点（SOE）。识别分子的 SOE 有助于解释与反应性代谢物相关的不良事件，并进行直接修饰以防止环氧化，从而获得更安全的药物。这项研究利用包含 702 个环氧化反应的数据库建立了一个模型，可以准确预测环氧化位点。该模型的基础是最初设计用于模拟细胞色素 P450 代谢位点（称为 XenoSite）的算法，该算法最近用于模拟不同分子与谷胱甘肽的内在反应性。该建模算法通过深度卷积网络系统地、定量地总结了数百个环氧化反应的知识。该网络在原子和分子水平上进行预测。用这种方法构建的最终环氧化模型识别出具有 94.9% 曲线下面积 (AUC) 性能的 SOE，并以 79.3% AUC 分离环氧化和非环氧化分子。此外，在环氧化分子内，该模型将芳香族或双键 SOE 与所有其他芳香族或双键分开，AUC 分别为 92.5% 和 95.1%。最后，该模型将 SOE 与 sp2 羟基化位点分离，AUC 为 83.2%。我们的模型是同类模型中的第一个，可能有助于开发更安全的药物。环氧化模型可在 http://swami.wustl.edu/xenosite 获取。环氧化物代谢物经常引起药物毒性。深度卷积网络准确预测类药物分子的环氧化。该模型可以指导修改候选药物以降低毒性的努力。
Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon–oxygen bonds. Epoxides are metabolites often formed by cytochromes P450 acting on aromatic or double bonds. The specific location on a molecule that undergoes epoxidation is its site of epoxidation (SOE). Identifying a molecule’s SOE can aid in interpreting adverse events related to reactive metabolites and direct modification to prevent epoxidation for safer drugs. This study utilized a database of 702 epoxidation reactions to build a model that accurately predicted sites of epoxidation. The foundation for this model was an algorithm originally designed to model sites of cytochromes P450 metabolism (called XenoSite) that was recently applied to model the intrinsic reactivity of diverse molecules with glutathione. This modeling algorithm systematically and quantitatively summarizes the knowledge from hundreds of epoxidation reactions with a deep convolution network. This network makes predictions at both an atom and molecule level. The final epoxidation model constructed with this approach identified SOEs with 94.9% area under the curve (AUC) performance and separated epoxidized and non-epoxidized molecules with 79.3% AUC. Moreover, within epoxidized molecules, the model separated aromatic or double bond SOEs from all other aromatic or double bonds with AUCs of 92.5% and 95.1%, respectively. Finally, the model separated SOEs from sites of sp2 hydroxylation with 83.2% AUC. Our model is the first of its kind and may be useful for the development of safer drugs. The epoxidation model is available at http://swami.wustl.edu/xenosite. Epoxide metabolites frequently cause drug toxicity. A deep convolution network accurately predicts the epoxidation of drug-like molecules. This model may guide efforts to modify drug candidates to be less toxic.