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Biomolecules 2018, 8(3), 85;

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

Swetox, Karolinska Institutet, Unit of Toxicology Sciences, SE-151 36 Södertälje, Sweden
Dept. Computer and Systems Sciences, Stockholm Univ., Box 7003, SE-164 07 Kista, Sweden
Leadscope, 1393 Dublin Road, Columbus, OH 43215, USA
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.
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)
Full-Text   |   PDF [835 KB, uploaded 29 August 2018]   |  


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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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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