Trapotsi Maria-Anna, Hosseini-Gerami Layla, Bender Andreas
Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
RSC Chem Biol. 2021 Dec 22;3(2):170-200. doi: 10.1039/d1cb00069a. eCollection 2022 Feb 9.
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.
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，但对于使表型研究结果合理化以及预测潜在副作用而言却很重要。生物信息学方法、机器学习技术的进步以及高通量数据在公共数据库中的不断积累，都为该领域的最新进展做出了重大贡献，但要确定在特定情况下最适合使用哪些数据和方法并非易事。在本综述中，我们重点关注这些方法和数据及其在生成MoA假设以供后续实验验证方面的应用。我们讨论了特定化合物的数据，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，如网络和通路数据，并提供了可获取这些数据的数据库信息。在方法学方面，我们既讨论了成熟的方法（连接性映射、通路富集），也讨论了更具发展潜力的方法（神经网络和多组学整合）。最后，我们回顾了通过整合多种数据模式和/或方法从计算分析中成功推断出化合物MoA的案例研究。我们撰写本综述的目的是让研究人员深入了解数据和方法在理解水平、偏差和解释方面的优缺点，并突出我们预计将改善作用机制阐明领域的未来研究方向，包括增加公众对能够进行数据整合的组学数据和方法的获取。
