Simplifying encoding of bioassays to accelerate translational drug discovery

描述(由申请人提供)：协作性药物发现公司(CDD)建议创建一个创新的软件模块，帮助生物学家快速、轻松地将他们的纯文本生物分析协议编码为适合计算处理的格式。该软件将使从事早期药物发现的科学家能够自动识别、分类和比较不同研究小组的数据集；高效和适当地记录实验程序；并制定分析项目工作流程战略和方案，将纯粹的研究工作转化为有效的翻译项目。为了鼓励采用，该软件将无缝集成到临床前数据管理平台(如CDD)中，优先考虑非信息学专家的科学家的直观易用性，与现有的实验室工作流程协调，最大限度地减少额外的注释工作，并作为综合体验的一部分为用户提供明确和即时的好处。这种新功能和极易使用的结合将加速转化药物发现工作，方法是使软件平台能够弥合生物学家和药物化学家之间的鸿沟，首次将复杂的工具也应用于生物方面，从而跨越这两个领域。现有的软件已经可以很容易地将筛选结果与化学结构联系起来。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。第一阶段的具体目标包括：1.原型一个新的注释引擎，它使用一个可表达的本体来交互地编码分析协议。该软件将在必要时询问用户，以获取无法从文本中自动推断的知识，但会尊重用户的时间价值 并施加最小的负担。2.与完全自动化的方法相比，在注解准确性方面有明显的质的改进。3.演示基于编码的化验描述符对CDD数据库中的数据集进行排序和比较。4.评估通过分析编码实现的其他好处，并确定要在第二阶段实施的功能的优先顺序。

DESCRIPTION (provided by applicant): Collaborative Drug Discovery, Inc. (CDD) proposes to create an innovative software module that will help biologists to quickly and easily encode their plain-text biological assay protocols into formats suitable for computational processing. The software will enable scientists engaged in early stage drug discovery to automatically identify, sort and compare datasets across research groups; efficiently and properly document experimental procedures; and formulate assay project workflow strategies and schema to transition pure research efforts into effective translational projects. In order to encourage adoption, the software will integrate seamlessly into preclinical data management platforms (such as CDD's), prioritize intuitive ease of use by scientists who are not informatics experts, harmonize with existing laboratory workflows, minimize the extra effort of annotation, 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. Specific aims for Phase 1 include: 1. Prototype a novel annotation engine that interactively encodes assay protocols using an expressive ontology. The software will query the user when necessary to capture knowledge that cannot be inferred automatically from the text, but will respect the value of the user's time and impose a minimal burden. 2. Show a clear, qualitative improvement in annotation accuracy compared with fully automated approaches. 3. Demonstrate sorting and comparing datasets within the CDD database based on encoded assay descriptors. 4. Evaluate other benefits enabled by assay encoding, and prioritize the features to be implemented in Phase 2.