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Application Note

Machine-learning prediction of drug mechanism of action from high-content cell painting images

In this application note, we explore the implementation of a deep-learning pipeline for applying high-content imaging in drug discovery, focusing on the study of the mechanisms of action (MOA) of chemical compounds. By implementing a semi-supervised machine-learning training method, we show an improvement in accuracy while preserving the ability to identify unknown MOAs. The result is a more accurate and flexible framework for phenotypic analysis.

For research use only. Not for use in diagnostic procedures.

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Machine-learning prediction of drug mechanism of action from high-content cell painting images

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