Comprehensive but simple encoding of bioassays to accelerate translational drug discovery

项目总结 协作式药物发现公司(CDD)建议扩展其创新的软件平台-生物检测 Express(BAE，1.0版)-它帮助生物学家快速、轻松地对他们的纯文本生物进行编码 将化验协议转换为适合计算处理的格式。在2B阶段，CDD将进一步增强 BAE，这样它就可以用一组全面、详细的注释来标记生物测试， 逐步指定协议。在其目前的第二阶段开发状态下，BAE 1.0使科学家能够 要总结指定实验性方案的英文文本，并补充 机器可解释的描述符。在2B阶段结束时，BAE 2.0将输出一台详细的机器- 可解释的版本将完全等同于并可以选择替代传统的 英语方法描述。 增强的BAE 2.0软件将使从事早期药物发现的科学家能够高效和 准确地记录实验过程，并智能地聚合各个研究小组的数据集。一个 重要的好处将是通过(1)标记差异来促进更好的访问、使用和再现性 密切相关的检测之间的差异；(2)将方案的差异与结果的差异相关联；以及(3) 提高在不同实验室进行的实验的可重复性，以突出潜在的原因 分歧。该项目将继续利用NIH和其他地方的相关广泛倡议，这些倡议是 致力于促进翻译研究，通过开放存储库收集筛选数据，并应用复杂的 本体来对这些数据集进行分类。我们的项目的目标是将这些不同的计划和 通过使他们的复杂的实践标准和指南现实地为研究人员所访问来统一它们 他们想要专注于他们的科学工作 为了鼓励采用，该软件将优先考虑非信息学科学家的直观易用性 专家，与现有的实验室工作流程协调，最大限度地减少注释的额外工作，集成 无缝集成到临床前数据管理平台(包括但不限于CDD自己的CDD库)， 并作为集成体验的一部分为用户提供清晰、即时的好处。 这种新功能和极易使用的结合将加速转化型药物的发现 通过增强软件平台的能力来弥合生物学家和药物化学家之间的分歧 将化学方面长期可用的尖端工具首次应用到生物方面， 从而跨越这两个领域。现有的软件已经可以很容易地将筛查结果与化学物质联系起来 结构。这一新平台将进一步将这些数据与屏幕的目的和方法联系起来。

PROJECT SUMMARY Collaborative Drug Discovery, Inc. (CDD) proposes to extend its innovative software platform — BioAssay Express (BAE, version 1.0) — which helps biologists to quickly and easily encode their plain-text biological assay protocols into formats suitable for computational processing. In Phase 2B, CDD will further enhance BAE so that it can markup a bioassay with a comprehensive, detailed set of annotations that completely specifies the protocol, step by step. In its current state of development in Phase 2, BAE 1.0 enables scientists to summarize the English language text that specifies an experimental protocol, supplementing it with machine-interpretable descriptors. At the end of Phase 2B, BAE 2.0 will output a detailed machine- interpretable version that will be completely equivalent to, and can optionally substitute for, the traditional English language method description. The enhanced BAE 2.0 software will enable scientists engaged in early stage drug discovery to efficiently and accurately document experimental procedures and intelligently aggregate datasets across research groups. An important benefit will be facilitating improved access, use and reproducibility by (1) flagging differences between closely related assays; (2) correlating differences in protocols with differences in results; and (3) improving the reproducibility of experiments conducted in different laboratories, to highlight potential causes of divergences. The project will continue to leverage the related broad initiatives at NIH and elsewhere that are working to promote translational research, gather screening data via open repositories, and apply sophisticated ontologies to classify these datasets. Our project targets the intersection of these disparate initiatives and unifies them by making their complex standards and guidelines for praxis realistically accessible to researchers who want to focus on their scientific work To encourage adoption, the software will prioritize intuitive ease of use by scientists who are not informatics experts, harmonize with existing laboratory workflows, minimize the extra effort of annotation, integrate seamlessly into preclinical data management platforms (including but not limited to CDD’s own CDD Vault), and deliver clear and immediate benefits to the user as part of an integrated experience. This combination of new capabilities and extreme ease of use will accelerate translational drug discovery efforts by empowering software platforms that bridge the divide between biologists and medicinal chemists to apply sophisticated tools — long available on the chemistry side — for the first time also to the biological side, and thus across both domains. Existing software can already easily connect screening results to chemical structures. This new platform will further connect these data to the purpose and methodology of the screens.