Uncertainty quantification in drug design
Reviewartikel, 2020

Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design–make–test–analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.


Lewis H. Mervin

AstraZeneca AB

Simon Johansson

AstraZeneca AB

Chalmers, Data- och informationsteknik, Data Science

Elizaveta Semenova

AstraZeneca AB

Kathryn A. Giblin

AstraZeneca AB

Ola Engkvist

AstraZeneca AB

Drug Discovery Today

1359-6446 (ISSN)

Vol. In Press


Produktionsteknik, arbetsvetenskap och ergonomi

Bioinformatik (beräkningsbiologi)






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