General data-analysis tools to relate chemical diversity

描述(申请人提供)：我们建议为定量化学生物学研究创造一个灵活而全面的软件环境。这种环境的一个主要目标将是帮助化学家进行合成规划，帮助生物学家使用这些研究人员熟悉的概念和工具来解释筛选结果和选择分子。通过设计考虑到这些研究人员的软件，我们将提供新颖的分析过程和访问以前只有计算科学家才能访问的关键工具。同时，通过在化学生物学家、计算科学家、统计学家和软件架构师之间建立紧密的合作，我们将提供更高端的功能以及与更复杂的用户最终需求的其他软件和数据源的互操作性。我们提出的信息模型和软件环境将允许实验科学家在与计算同行相同的概念框架内工作，弥合理论和实验之间的关键差距，并将每种方法的优势应用于常见问题。易于使用的软件，用于多样性分析、合成试剂选择和确定与测量结果相关的结构特征，将把重要的决策工具交到研究界手中，对于根据NIH路线图分子图书馆倡议(MLI)的目标合成分子的化学家和选择分子的生物学家特别有用。 我们已经组建了一个多学科团队，该团队唯一有资格提供此类软件的原因有三个。首先，我们在实验化学遗传学领域有相当多的经验，包括计划、合成以及将化学结构与生物结果联系起来。其次，我们有为化学和生物信息学开发网络软件的经验。第三，我们居住在一个丰富的智力环境中(哈佛大学和麻省理工学院的博德研究所)，这是生物信息学和基因组医学的卓越中心，这将在软件开发期间提供对丰富的实验设计策略和数据分析方法的访问。通过向实验者部署关键的软件工具，我们将开发实用的用例和接口要求，以指导当前和未来NIH路线图MLI活动的核心软件的进一步开发。我们努力的一个主要目标是将新的化学和化学生物学工具与现有的基因组医学分析工具相结合，扩大化学工具的覆盖范围，使生物信息学和临床研究人员能够对公共健康产生越来越大的影响。

DESCRIPTION (provided by applicant): We propose to create a flexible but comprehensive software environment for quantitative chemical biology research. A primary goal of this environment will be to aid chemists in synthetic planning, and biologists in interpreting screening results and selecting molecules, using concepts and tools familiar to these researchers. By designing software with these researchers in mind, we will provide both novel analytical processes and access to critical tools that have previously been accessible only to computational scientists. Simultaneously, by building tight collaborations between chemical biologists, computational scientists, statisticians, and software architects, we will provide higher-end functionality and interoperability with other software and data sources that more sophisticated users will ultimately demand. Our proposed information model and software environment will allow experimental scientists to work within the same conceptual framework as their computational counterparts, bridging critical gaps between theory and experiment, and applying the strengths of each approach to common problems. Easy-to-use software for diversity analysis, synthetic reagent selection, and determination of structural features relevant to measured outcomes, will place important decision-making tools into the hands of the research community, and be especially useful for chemists synthesizing molecules, and biologists selecting molecules, pursuant to the goals of the NIH Road Map Molecular Libraries Initiative (MLI). We have assembled a multidisciplinary team that is uniquely qualified to provide such software for three reasons. First, we have considerable experience in the field of experimental chemical genetics, including planning, synthesis, and relating chemical structure to biological outcomes. Second, we have experience developing web-enabled software for both chemistry and bioinformatics. Third, we reside within a rich intellectual environment (The Broad Institute of Harvard and MIT) that is a center of excellence in bioinformatics and genomic medicine, and that will provide access during software development to a wealth of experimental design strategies and data analysis methods. By deploying key software tools to experimentalists, we will,develop practical use-cases and interface requirements that will guide further development of software central to present and future NIH Road Map MLI activities. A primary goal of our effort will be to integrate new tools for chemistry and chemical biology with existing analysis tools for genomic medicine, expanding the reach of chemistry tools to enable bioinformatics and clinical researchers to have increasing impact on public health.