Ni Eric, Kwon Eehjoe, Young Lauren M, Felsovalyi Klara, Fuller Jennifer, Cardozo Timothy
NYU Langone Health, Department of Biochemistry & Molecular Pharmacology, New York, NY 10016, USA.
Genecentrix Inc, New York, NY 10014, USA.
Future Drug Discov. 2020 Feb 5;2(1):FDD26. doi: 10.4155/fdd-2019-0032.
High-throughput phenotypic screens have emerged as a promising avenue for small-molecule drug discovery. The challenge faced in high-throughput phenotypic screens is target deconvolution once a small molecule hit is identified. Chemogenomics libraries have emerged as an important tool for meeting this challenge. Here, we investigate their target-specificity by deriving a 'polypharmacology index' for broad chemogenomics screening libraries.
All known targets of all the compounds in each library were plotted as a histogram and fitted to a Boltzmann distribution, whose linearized slope is indicative of the overall polypharmacology of the library.
RESULTS & CONCLUSION: Comparison of libraries clearly distinguished the most target-specific library, which might be assumed to be more useful for target deconvolution in a phenotypic screen.
高通量表型筛选已成为小分子药物发现的一条有前景的途径。高通量表型筛选面临的挑战是一旦鉴定出小分子活性物质，就要进行靶点反卷积。化学基因组学文库已成为应对这一挑战的重要工具。在此，我们通过为广泛的化学基因组学筛选文库推导一个“多药理学指数”来研究它们的靶点特异性。
将每个文库中所有化合物的所有已知靶点绘制成直方图，并拟合到玻尔兹曼分布，其线性化斜率表明文库的整体多药理学特性。
文库之间的比较清楚地区分出了靶点特异性最强的文库，该文库可能被认为在表型筛选中对靶点反卷积更有用。
