Image-based profiling for drug discovery: due for a machine-learning upgrade?
By Srinivas Niranj Chandrasekaran, Hugo Ceulemans, Justin D. Boyd, Anne E Carpenter. Nature reviews. Drug discovery, 2020
355 citations recorded by the source corpus.
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. Image-based profiling is a strategy to mine the rich information in biological images. Carpenter and colleagues discuss how the application of machine learning is renewing interest in image-based profiling for all aspects of the drug discovery process, from understanding disease mechanisms to predicting a drug’s activity or mechanism of action.
Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/082c82bfec1b087395db9ff377131600ac75b8e4
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Citations in the corpus
- OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization (2022, background): “Recent developments in image analysis and machine learning offer new opportunities to extract high-dimensional features from microscopy images ( 50 , 51 ).”