Bender Andreas, Young Daniel W, Jenkins Jeremy L, Serrano Martin, Mikhailov Dmitri, Clemons Paul A, Davies John W
Lead Discovery Informatics, Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Ave., Cambridge, MA 02139, USA.
Comb Chem High Throughput Screen. 2007 Sep;10(8):719-31. doi: 10.2174/138620707782507313.
Chemogenomics comprises a systematic relationship between targets and ligands that are used as target modulators in living systems such as cells or organisms. In recent years, data on small molecule-bioactivity relationships have become increasingly available, and consequently so have the number of approaches used to translate bioactivity data into knowledge. This review will focus on two aspects of chemogenomics. Firstly, in cases such as cell-based screens, the question of which target(s) a compound is modulating in order to cause the observed phenotype is crucial. In silico target prediction tools can suggest likely biological targets of small molecules via data mining in target-annotated chemical databases. This review presents some of the current tools available for this task and shows some sample applications relevant to a pharmaceutical industry setting. These applications are the prediction of false-positives in cell-based reporter gene assays, the prediction of targets by linking bioassay data with protein domain annotations, and the direct prediction of adverse reactions. Secondly, in recent years a shift from structure-derived chemical descriptors to biological descriptors has occurred. Here, the effect of a compound on a number of biological endpoints is used to make predictions about other properties, such as putative targets, associated adverse reactions, and pathways modulated by the compound. This review further summarizes these "performance" descriptors and their applications, focusing on gene expression profiles and high-content screening data. The advent of such biological fingerprints suggests that the field of drug discovery is currently at a crossroads, where single target bioassay results are supplanted by multidimensional biological fingerprints that reflect a new awareness of biological networks and polypharmacology.
化学基因组学包含了靶点与配体之间的系统关系，这些配体在细胞或生物体等生命系统中用作靶点调节剂。近年来，小分子-生物活性关系的数据越来越多，因此将生物活性数据转化为知识的方法数量也随之增加。本综述将聚焦于化学基因组学的两个方面。首先，在基于细胞的筛选等情况下，确定化合物为引起观察到的表型而调节的靶点至关重要。计算机靶点预测工具可以通过在带有靶点注释的化学数据库中进行数据挖掘，来推测小分子可能的生物学靶点。本综述介绍了一些目前可用于此任务的工具，并展示了一些与制药行业相关的示例应用。这些应用包括基于细胞的报告基因检测中假阳性的预测、通过将生物测定数据与蛋白质结构域注释相联系来预测靶点，以及不良反应的直接预测。其次，近年来已发生了从基于结构的化学描述符向生物学描述符的转变。在此，化合物对多个生物学终点的影响被用于预测其他性质，如假定靶点、相关不良反应以及化合物调节的途径。本综述进一步总结了这些“性能”描述符及其应用，重点关注基因表达谱和高内涵筛选数据。此类生物学指纹图谱的出现表明，药物发现领域目前正处于一个十字路口，单一靶点生物测定结果正被反映对生物网络和多药理学新认识的多维生物学指纹图谱所取代。
