Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation, both experimental and computational screening assays are utilized. Experimental assays for assessing the formation of reactive metabolites are low throughput and expensive to perform, so they are often reserved until later stages of the drug development pipeline when the drug candidate pools are already significantly narrowed. In contrast, computational methods are high throughput and cheap to perform to screen thousands to millions of compounds for potentially toxic molecules during the early stages of the drug development pipeline. Commonly used computational methods focus on detecting and structurally characterizing reactive metabolite–biomolecule adducts or predicting sites on a drug molecule that are liable to form reactive metabolites. However, such methods are often only concerned with the structure of the initial drug molecule or of the adduct formed when a biomolecule conjugates to a reactive metabolite. Thus, these methods are likely to miss intermediate metabolites that may lead to subsequent reactive metabolite formation. To address these shortcomings, we create XenoNet, a metabolic network predictor, that can take a pair of a substrate and a target product as input and (1) enumerate pathways, or sequences of intermediate metabolite structures, between the pair, and (2) compute the likelihood of those pathways and intermediate metabolites. We validate XenoNet on a large, chemically diverse data set of 17 054 metabolic networks built from a literature-derived reaction database. Each metabolic network has a defined substrate molecule that has been experimentally observed to undergo metabolism into a defined product metabolite. XenoNet can predict experimentally observed pathways and intermediate metabolites linking the input substrate and product pair with a recall of 88 and 46%, respectively. Using likelihood scoring, XenoNet also achieves a top-one pathway and intermediate metabolite accuracy of 93.6 and 51.9%, respectively. We further validate XenoNet against prior methods for metabolite prediction. XenoNet significantly outperforms all prior methods across multiple metrics. XenoNet is available at https://swami.wustl.edu/xenonet.

中文翻译：

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XenoNet：中间代谢物形成的推断和可能性。

药物代谢是药物不良反应的常见原因。药物分子可以代谢成反应性代谢物，这些代谢物可以在称为生物活化的过程中与生物分子（如蛋白质和 DNA）结合。为了减轻生物活化引起的不良反应，采用了实验和计算筛选测定法。用于评估反应性代谢物形成的实验测定通量低且执行成本昂贵，因此它们通常被保留到药物开发流程的后期阶段，此时候选药物池已经显着缩小。相比之下，计算方法吞吐量高且成本低廉，可在药物开发流程的早期阶段筛选数千至数百万种化合物中的潜在有毒分子。常用的计算方法侧重于检测和结构表征反应性代谢物-生物分子加合物或预测药物分子上易于形成反应性代谢物的位点。然而，此类方法通常仅涉及初始药物分子的结构或生物分子与反应性代谢物缀合时形成的加合物的结构。因此，这些方法可能会错过可能导致随后反应性代谢物形成的中间代谢物。为了解决这些缺点，我们创建了 XenoNet，一种代谢网络预测器，它可以将一对底物和一个目标产物作为输入，并且 (1) 枚举这对之间的途径或中间代谢物结构序列，以及 (2)计算这些途径和中间代谢物的可能性。我们在由文献衍生的反应数据库构建的由 17054 个代谢网络组成的大型化学多样化数据集上验证了 XenoNet。每个代谢网络都有一个确定的底物分子，经实验观察，该分子会代谢成确定的产物代谢物。XenoNet 可以预测实验观察到的连接输入底物和产物对的途径和中间代谢物，召回率分别为 88% 和 46%。使用似然评分，XenoNet 还分别实现了 93.6% 和 51.9% 的顶级途径和中间代谢物准确度。我们进一步根据现有的代谢物预测方法验证 XenoNet。XenoNet 在多个指标上显着优于所有先前的方法。XenoNet 可在 https://swami.wustl.edu/xenonet 上获取。