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- W3107587236 endingPage "489" @default.
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- W3107587236 abstract "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." @default.
- W3107587236 created "2020-12-07" @default.
- W3107587236 creator A5037160846 @default.
- W3107587236 creator A5076975589 @default.
- W3107587236 creator A5084227461 @default.
- W3107587236 creator A5086142243 @default.
- W3107587236 creator A5088554828 @default.
- W3107587236 date "2021-02-01" @default.
- W3107587236 modified "2023-10-13" @default.
- W3107587236 title "Uncertainty quantification in drug design" @default.
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