公平数据管理:对药物发现意味着什么?

Yojana Gadiya, V. Ioannidis, David Henderson, P. Gribbon, P. Rocca-Serra, V. Satagopam, Susanna-Assunta Sansone, Wei Gu
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引用次数: 0

摘要

药物发现界在将安全有效的药物推向市场方面面临着高昂的成本,部分原因是在研究和开发过程中必须产生的数据数量和复杂性不断增加。充分利用这些昂贵的实验和计算数据资源已成为科学家的关键目标,因为利用人工智能(AI)和基于机器学习的分析的力量来解决药物发现中固有的复杂问题是明确的必要条件。反过来,人工智能方法严重依赖于底层训练数据的数量、质量、一致性和范围。虽然现有的临床前和临床数据不能完全取代项目中从头生成数据的需求,但访问相关的历史数据是一项有价值的资产,因为它的重用可以减少执行类似实验的需要,从而避免“重新发明轮子”的情况。不幸的是,大多数合适的数据资源通常都保存在研究所、公司或个人研究小组中,因此无法为更广泛的社区所用。因此,使数据可查找、可访问、可互操作和可重复使用(FAIR)对于更广泛的药物发现和开发科学家社区来说至关重要,以便他们从所做的工作中学习,并利用这些发现来增强对自己研究成果的理解。在这篇小型综述中,我们阐明了FAIR数据管理在药物发现管道中的效用,并评估了这些FAIR数据对药物开发过程的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FAIR data management: what does it mean for drug discovery?
The drug discovery community faces high costs in bringing safe and effective medicines to market, in part due to the rising volume and complexity of data which must be generated during the research and development process. Fully utilising these expensively created experimental and computational data resources has become a key aim of scientists due to the clear imperative to leverage the power of artificial intelligence (AI) and machine learning-based analyses to solve the complex problems inherent in drug discovery. In turn, AI methods heavily rely on the quantity, quality, consistency, and scope of underlying training data. While pre-existing preclinical and clinical data cannot fully replace the need for de novo data generation in a project, having access to relevant historical data represents a valuable asset, as its reuse can reduce the need to perform similar experiments, therefore avoiding a “reinventing the wheel” scenario. Unfortunately, most suitable data resources are often archived within institutes, companies, or individual research groups and hence unavailable to the wider community. Hence, enabling the data to be Findable, Accessible, Interoperable, and Reusable (FAIR) is crucial for the wider community of drug discovery and development scientists to learn from the work performed and utilise the findings to enhance comprehension of their own research outcomes. In this mini-review, we elucidate the utility of FAIR data management across the drug discovery pipeline and assess the impact such FAIR data has made on the drug development process.
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