Computational analyses of mechanism of action (MoA): data, methods and integration.
Computational analyses of mechanism of action (MoA): data, methods and integration.
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DOI：
10.1039/d1cb00069a
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发表时间：
2022-02-09
影响因子：
4.1
通讯作者：
Bender A
中科院分区：
文献类型：
--
作者：
Trapotsi MA;Hosseini-Gerami L;Bender A
阐明化合物的作用机制（MoA）是药物发现过程中的一项具有挑战性的任务，但为了合理化表型发现并预测潜在的副作用，这一点很重要。生物信息学方法，机器学习技术的进步以及公共数据库中高通量数据的不断增加为该领域的最新进展做出了重大贡献，但要决定哪些数据和方法最适合用于特定情况并不简单。在这篇综述中，我们专注于这些方法和数据及其在生成MoA假说的后续实验验证中的应用。我们讨论化合物的具体数据，如组学，细胞形态和生物活性数据，以及常用的补充先验知识，如网络和途径数据，并提供信息的数据库，这些数据可以访问。在方法论方面，我们讨论了成熟的方法（连接映射，途径富集）以及更发展的方法（神经网络和多组学集成）。最后，我们回顾了案例研究，其中化合物的MoA成功地建议从计算分析，结合多种数据模式和/或方法。我们这次审查的目的是为研究人员提供深入了解的好处和缺点的数据和方法的理解水平，偏见和解释-并强调未来的调查途径，我们预计将改善该领域的MoA阐明，包括更多的公众访问-组学数据和方法，能够数据集成。本文综述了不同的数据，数据资源和方法的计算作用机制（MoA）分析，并强调了一些案例研究中的数据类型和方法的集成，使MoA阐明的系统水平。
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation – and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. This review summarises different data, data resources and methods for computational mechanism of action (MoA) analysis, and highlights some case studies where integration of data types and methods enabled MoA elucidation on the systems-level.