Image-based profiling for drug discovery: due for a machine-learning upgrade?

Journal: Nature reviews. Drug discovery
Published Date:

Abstract

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

Authors

  • Srinivas Niranj Chandrasekaran
    Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Hugo Ceulemans
    Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340 Beerse, Belgium. Electronic address: hceulema@its.jnj.com.
  • Justin D Boyd
    High Content Imaging Technology Center, Internal Medicine Research Unit, Pfizer Inc., Cambridge, MA, USA.
  • Anne E Carpenter
    The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States. Electronic address: anne@broadinstitute.org.