Increasing amounts of biological data are accumulating in the pharmaceutical industry and academic institutions. However, data does not equal actionable information, and guidelines for appropriate data capture, harmonization, integration, mining, and visualization need to be established to fully harness its potential. Here, we describe ongoing efforts at Merck & Co. to structure data in the area of chemogenomics. We are integrating complementary data from both internal and external data sources into one chemogenomics database (Chemical Genetic Interaction Enterprise; CHEMGENIE). Here, we demonstrate how this well-curated database facilitates compound set design, tool compound selection, target deconvolution in phenotypic screening, and predictive model building.

越来越多的生物学数据在制药行业和学术机构中积累。但是，数据并不等同于可操作的信息，因此需要建立适当的数据捕获，统一，集成，挖掘和可视化的指南，以充分利用其潜力。在这里，我们描述了默克公司为构建化学基因组学领域的数据而正在进行的工作。我们正在将来自内部和外部数据源的补充数据集成到一个化学基因组学数据库（Chemical Genetic Interaction Enterprise； CHEMGENIE）中。在这里，我们演示了这个精心挑选的数据库如何促进化合物集设计，工具化合物选择，表型筛选中的目标去卷积以及预测模型的建立。