Capturing Structure−Activity Relationships from Chemogenomic Spaces

Modeling off-target effects is one major goal of chemical biology, particularly in its applications to drug discovery. Here, we describe a new approach that allows the extraction of structure−activity relationships from large chemogenomic spaces starting from a single chemical structure. Several public source databases, offering a vast amount of data on structure and activity for a large number of different targets, have been investigated for their usefulness in automated structure−activity relationships (SAR) extraction. SAR tables were constructed by assembling similar structures around each query structure that have an activity record for a particular target. Quantitative series enrichment analysis (QSEA) was applied to these SAR tables to identify trends and to transform these trends into topomer CoMFA models. Overall more than 1700 SAR tables with topomer CoMFA models have been obtained from the ChEMBL, PubChem, and ChemBank databases. These models were able to highlight the structural trends associa...