药物作用机制自动优先级知识网络的设计与应用。
Design and application of a knowledge network for automatic prioritization of drug mechanisms.
机构信息
Department of Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
出版信息
Bioinformatics. 2022 May 13;38(10):2880-2891. doi: 10.1093/bioinformatics/btac205.
MOTIVATION
Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications.
RESULTS
In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet's ability to use mechanistic insight to identify a drug's mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug-target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method's utility in providing mechanistic insight into repurposing predictions it provides.
AVAILABILITY AND IMPLEMENTATION
The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet (archived at https://doi.org/10.5281/zenodo.6456335).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
由于将药物推向市场的时间和成本降低，药物重新定位是一种有吸引力的替代从头发现药物的方法。计算药物重定位方法，特别是可以解释和预测药物机制的非黑盒方法，可能为指导未来的发展提供巨大的好处。通过调整数据和算法以利用与药物机制相关的重要关系，可以训练计算药物重定位算法来预测和解释机制新颖的适应症。
结果
在这项工作中，我们检查了药物机制数据库（DrugMechDB）中发现的 123 个经过精心整理的药物机制途径，在确定了最重要的关系之后，我们整合了 18 个数据源来创建一个异构知识库 MechRepoNet，能够捕获这些途径中的信息。我们仅使用已知具有机制性质的关系子集将 Rephetio 重新定位算法应用于 MechRepoNet，在评估集上的 AUC 值为 0.83，具有足够的预测能力。由此产生的重新定位模型使我们能够对我们知识图谱中的路径进行优先级排序，以生成预测的治疗机制。我们发现，当网络中存在 DrugMechDB 路径时，预测机制的评价很高。然后，我们证明了 MechRepoNet 能够利用机制洞察力来识别药物的机制靶点，在已知药物-靶点相互作用的测试集中平均倒数排名为 0.525。最后，我们通过重新定位抗癌药物伊马替尼治疗哮喘和氨苯蝶啶治疗骨质疏松症的实例，展示了这种方法在提供对其提供的重新定位预测的机制洞察力方面的实用性。
可用性和实现
可在 https://github.com/SuLab/MechRepoNet（存档于 https://doi.org/10.5281/zenodo.6456335）上获得重现本分析的全部内容的 Python 代码。
补充信息
补充数据可在生物信息学在线获得。
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