Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.
Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.
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DOI：
10.1021/acs.jcim.0c00360
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发表时间：
2020-10-26
影响因子：
5.6
通讯作者：
Swamidass SJ
中科院分区：
文献类型：
--
作者：
Hughes TB;Dang NL;Kumar A;Flynn NR;Swamidass SJ
药物不良代谢通常严重影响患者的发病率和死亡率。不幸的是，药物代谢实验测定是昂贵的，低效的，和缓慢的。相反，计算建模可以在药物开发的早期阶段迅速标记出数千种候选药物中的潜在毒性分子。大多数代谢模型侧重于预测代谢位点（SOM）：代谢酶靶向的特定底物原子。然而，SOM仅仅是代谢结构的代理：SOM的知识并不能明确地提供实际的代谢物结构。如果没有明确的代谢物结构，计算系统就无法评估新分子的性质。例如，代谢物的反应性不能自动预测，这是一个关键的限制，因为反应性药物代谢物是药物不良反应（ADR）的关键驱动因素。此外，无法预测进一步的代谢事件，即使大多数底物的代谢途径包括两个或更多个连续步骤。为了克服SOM范式的近视，本研究构建了一个定义明确的系统，称为代谢森林，用于生成精确的代谢物结构。我们验证了代谢森林的底物和产品结构从一个大的，化学多样性，文献衍生的数据集的20 736条记录。代谢森林为79.42%的这些记录找到了连接每个底物和产物的途径。通过执行深度为2或3的广度优先搜索，我们将性能分别提高到88.43%和88.77%。代谢森林包括一个专门的算法，用于产生准确的醌结构，最常见的类型的反应代谢物。据我们所知，这种醌结构算法是同类算法中的第一个，因为醌形成的不同机制很难系统地再现。我们在先前发表的576个醌反应的数据集上验证了代谢森林，预测它们的结构，深度三性能为91.84%。代谢森林准确地列举了代谢物结构，使有前途的新方向，如联合代谢和反应建模。
Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule’s properties. For example, the metabolite’s reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system—termed the metabolic forest—for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.
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影响因子：
8.6
作者：
Djoumbou-Feunang, Yannick;Fiamoncini, Jarlei;Wishart, David S.
通讯作者：
Wishart, David S.
影响因子：
1.6
作者：
Basak, G. W.;Drozd-Sokolowska, J.;Wiktor-Jedrzejczak, W.
通讯作者：
Wiktor-Jedrzejczak, W.
影响因子：
4.1
作者：
Delaine, Tamara;Ponting, David J.;Karlberg, Ann-Therese
通讯作者：
Karlberg, Ann-Therese
影响因子：
2.4
作者：
Alempijevic T;Zec S;Milosavljevic T
通讯作者：
Milosavljevic T
影响因子：
3.8
作者：
Adams, David H.;Ju, Cynthia;Jaeschke, Hartmut
通讯作者：
Jaeschke, Hartmut