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Connecting synthetic chemistry decisions to cell and genome biology using small-molecule phenotypic profiling

机译:使用小分子表型分析将合成化学决策与细胞和基因组生物学联系起来

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Discovering small-molecule modulators for thousands of gene products requiresmultiple stages ofbiological testing, specificity evaluation, and chemical optimization. Many cellular profiling methods, including cellular sensitivity, gene expression, and cellular imaging, have emerged as methods to assess the functional consequences of biological perturbations. Cellular profiling methods applied to small-molecule science provide opportunities to use complex phenotypic information to prioritize and optimize small-molecule structures simultaneously against multiple biological endpoints. As throughput increases and cost decreases for such technologies, we see an emerging paradigm of using more information earlier in probe-discovery and drugdiscovery efforts. Moreover, increasing access to public datasets makes possible the construction of ‘virtual’ profiles of small-molecule performance, even when multiplexed measurements were not performed or when multidimensional profiling was not the original intent. We review some key conceptual advances in small-molecule phenotypic profiling, emphasizing connections to other information, such as proteinbinding measurements, genetic perturbations, and cell states. We argue that to maximally leverage these measurements in probe-discovery and drug-discovery requires a fundamental connection to synthetic chemistry, allowing the consequences of synthetic decisions to be described in terms of changes insmallmolecule profiles. Mining such data in the context of chemical structure and synthesis strategies can inform decisions about chemistry procurement and library development, leading to optimal small-molecule screening collections.
机译:发现用于数千种基因产物的小分子调节剂需要多个阶段的生物学测试,特异性评估和化学优化。已经出现了许多细胞谱分析方法,包括细胞敏感性,基因表达和细胞成像,作为评估生物扰动功能后果的方法。应用于小分子科学的细胞谱分析方法提供了机会,可以使用复杂的表型信息来针对多个生物学终点同时优化和优化小分子结构。随着此类技术的通量增加和成本降低,我们看到了在探针发现和药物发现工作中更早使用更多信息的新兴范例。此外,即使不执行多路复用测量或并非以多维轮廓分析为初衷,对公共数据集的越来越多的访问也可以构建小分子性能的“虚拟”曲线。我们回顾了小分子表型分析中的一些关键概念进展,强调了与其他信息的连接,例如蛋白结合测量,遗传扰动和细胞状态。我们认为,要在探针发现和药物发现中最大程度地利用这些测量值,就需要与合成化学建立基础联系,从而可以根据小分子谱图的变化来描述合成决策的后果。在化学结构和合成策略的背景下挖掘此类数据可以为有关化学采购和文库开发的决策提供依据,从而实现最佳的小分子筛选收集。

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