Informing synthetic decision making using cheminformatic and bioinformatic profil

使用化学信息学和生物信息学分析为综合决策提供信息

• 批准号: 8331513
• 负责人: PAUL ANDREW CLEMONS
• 金额: $ 19.79万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8331513
• 关键词: Biodiversity; Bioinformatics; Biological; Biological Assay; Biological Testing; Biology; Cells; Chemical Structure; Chemicals; Collection; Communities; Computer Analysis; Constitution; Coupling; Data; Data Analyses; Data Set; Decision Making; Descriptor; Disease; Elements; Ensure; Enzyme Inhibition; Feedback; Gene Expression; Heart; Image; Learning; Libraries; Measurement; Measures; Methods; Molecular; Outcome; Outcome Measure; Pathway interactions; Performance; Property; Research; S phase; Screening procedure; Shapes; Speed; Structure; Structure-Activity Relationship; Synthesis Chemistry; base; cheminformatics; data sharing; drug discovery; method development; molecular shape; research study; skeletal; small molecule; stereochemistry; tool

Cheminformatics can be conceptualized as attempting to learn predictable relationships between measured performance of small molecules and their chemical structures. In this context, chemical structure typically is represented by a numerical description of molecular constitution, connectivity, size, shape, and reactivity of small molecules. However, the most widely used molecular descriptions eschew three-dimensional considerations such as stereochemistry and molecular shape in favor of speed of calculation, and almost no such descriptions explicitly consider the strategies underlying small-molecule syntheses. This is particularly problematic for our CMLD, since stereochemistry and small-molecule synthesis lie at the heart of our research. Moreover, most existing cheminformatic approaches consider multiple structural features of small molecules in the context of a single performance measurement (e.g., enzyme inhibition). Increasingly, small-molecule screening exploits the ability to make multiple measurements per well (e.g., gene expression- or image-based screening) or in parallel. Such datasets provide a unique opportunity for Cheminformatics research - the relevance of quantitative descriptions of chemical structure, and their connections with synthetic planning, can be judged using actual performance of small molecules in multiple biological assays. Project 4 aims to exploit this opportunity. Direct modulations with small molecules of cellular pathways that underlie phenotypic change make it possible to dissect cell circuitry and even disease biology. To enable discovery and optimization, both chemical biology and drug discovery rely on the chemical similarity principle - small molecules with similar structures have similar performance. Multidimensional datasets reflecting actual performance provide a computational basis to find structural descriptions whose similarities best accord with similarities in small-molecule performance. In turn, these descriptions can be used to guide synthetic chemistry planning by calculation of relevant structural properties for new small molecules in advance of their synthesis. Such descriptions of structure could provide guidance to synthetic chemists seeking to make a small-molecule screening collection with optimal properties. Particularly in the pilot synthesis phase, where relatively small numbers of compounds are made available for biological testing, it is important to plan syntheses such that maximal information about structure/activity relationships can be learned from pilot screening experiments. In the context of the build/couple/pair (B/C/P) strategy for diversity synthesis that is being explored in our CMLD (Projects 1 and 2, supported by Project 3), direct computational connections between synthetic choices and descriptors of molecular complexity, stereochemistry, and shape will help guide synthetic planning. Coupling biological performance annotation to subsets of molecules realized as pilot libraries will provide guidance (e.g., to the Library Synthesis Core) as to which candidate small-molecule collections will have the highest likelihood of biological activities and diversity of biological activities.

化学信息学可以被概念化为试图了解被测量的 小分子及其化学结构的性能。在这种情况下，化学结构通常是 由分子组成、连接性、大小、形状和反应性的数字描述表示 小分子。然而，最广泛使用的分子描述避开了三维 考虑立体化学和分子形状等有利于计算速度的因素，几乎没有 这样的描述明确考虑了小分子合成的基本策略。这是特别的 这对我们的CMLD来说是个问题，因为立体化学和小分子合成是我们研究的核心。 此外，大多数现有的化学信息学方法考虑了小分子的多种结构特征 单一性能测量的背景(例如，酶抑制)。越来越多的小分子 筛查利用了每口井进行多次测量的能力(例如，基于基因表达或基于图像 筛选)或并行进行。这样的数据集为化学信息学研究提供了独特的机会-- 化学结构的定量描述及其与综合规划的联系的相关性可以 在多种生物检测中使用小分子的实际性能来判断。项目4旨在利用 这个机会。 作为表型改变基础的小分子细胞通路的直接调节使其成为可能 来剖析细胞电路，甚至疾病生物学。为了实现发现和优化，化学生物学 药物的发现依赖于化学相似原理--具有相似结构的小分子 类似的表现。反映实际性能的多维数据集提供了计算基础 找出其相似性最符合小分子性能相似性的结构描述。在……里面 反过来，这些描述可以通过相关结构的计算来指导合成化学规划 新的小分子在合成之前的性质。 这种对结构的描述可以为寻求制造小分子的合成化学家提供指导 筛选具有最佳性能的集合。特别是在试点综合阶段，这一阶段相对较小 许多化合物可用于生物测试，重要的是计划合成，使其 有关结构/活性关系的最大信息可以从中试筛选实验中获得。在……里面 我们的CMLD正在探索构建/对/对(B/C/P)分集合成战略的背景 (项目1和项目2，项目3支持)，综合选择和综合选择之间的直接计算联系 分子复杂性、立体化学和形状的描述符将有助于指导合成规划。偶联 实现为先导库的分子子集的生物学性能注释将提供指导(例如， 到图书馆合成核心)，关于哪些候选小分子收藏将拥有最高的 生物活动的可能性和生物活动的多样性。