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ACS Chem Biol. 2018 Oct 19;13(10):2819-2821. doi: 10.1021/acschembio.8b00881.

Adversarial Controls for Scientific Machine Learning.

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Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases and Bakar Institute for Computational Health Sciences , University of California-San Francisco , 675 Nelson Rising Lane , San Francisco , California 94158 , United States.


New machine learning methods to analyze raw chemical and biological data are now widely accessible as open-source toolkits. This positions researchers to leverage powerful, predictive models in their own domains. We caution, however, that the application of machine learning to experimental research merits careful consideration. Machine learning algorithms readily exploit confounding variables and experimental artifacts instead of relevant patterns, leading to overoptimistic performance and poor model generalization. In parallel to the strong control experiments that remain a cornerstone of experimental research, we advance the concept of adversarial controls for scientific machine learning: the design of exacting and purposeful experiments to ensure that predictive performance arises from meaningful models.

[Indexed for MEDLINE]

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