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Open AccessFeature PaperArticle

Predicting Aromatic Amine Mutagenicity with Confidence: A Case Study Using Conformal Prediction

1
Swetox, Karolinska Institutet, Unit of Toxicology Sciences, SE-151 36 Södertälje, Sweden
2
Dept. Computer and Systems Sciences, Stockholm Univ., Box 7003, SE-164 07 Kista, Sweden
3
Leadscope, 1393 Dublin Road, Columbus, OH 43215, USA
4
Drug Safety and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, SE-431 83 Mölndal, Sweden
*
Author to whom correspondence should be addressed.
Biomolecules 2018, 8(3), 85; https://doi.org/10.3390/biom8030085
Received: 26 June 2018 / Revised: 16 August 2018 / Accepted: 21 August 2018 / Published: 29 August 2018
(This article belongs to the Special Issue Machine Learning for Molecular Modelling in Drug Design)
The occurrence of mutagenicity in primary aromatic amines has been investigated using conformal prediction. The results of the investigation show that it is possible to develop mathematically proven valid models using conformal prediction and that the existence of uncertain classes of prediction, such as both (both classes assigned to a compound) and empty (no class assigned to a compound), provides the user with additional information on how to use, further develop, and possibly improve future models. The study also indicates that the use of different sets of fingerprints results in models, for which the ability to discriminate varies with respect to the set level of acceptable errors. View Full-Text
Keywords: aromatic amines; mutagenicity; conformal prediction; confidence; random forest aromatic amines; mutagenicity; conformal prediction; confidence; random forest
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MDPI and ACS Style

Norinder, U.; Myatt, G.; Ahlberg, E. Predicting Aromatic Amine Mutagenicity with Confidence: A Case Study Using Conformal Prediction. Biomolecules 2018, 8, 85.

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