Understanding compound mechanism-of-action (MOA) is critical in early drug discovery and the BioMAP system<sup>1</sup> is one of the experimental panels that allows a rapid assessment of possible MOAs. It is defined by primary human cell disease models derived from multiple tissues that represent a wide range of biology and offers insights on compound’s on/off-target mechanisms by measuring several biomarkers. The profiling of hit compounds allows for better prioritization of molecular series in early drug discovery efforts. In this poster, we will outline a cheminformatics analysis to understand the relationship between chemical space and this biological space defined by the BioMAP biomarkers. In addition, we will highlight how the application of active machine learning in combination with physico-chemical property rules could help the selection of hit compounds for experimental BioMAP profiling allowing for an efficient and cost effective biological profiling.<br/><br/>

Cheminformatics analysis of in-house translational biomarkers BioMAP database

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.