Progress in computational toxicology

Journal of Pharmacological and Toxicological Methods
Sean Ekins

Abstract

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed. A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods. The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data. Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry...Continue Reading

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Citations

Jun 20, 2014·Journal of Computer-aided Molecular Design·Sean EkinsAntony J Williams
Oct 1, 2015·Briefings in Bioinformatics·Graciela H GonzalezCasey S Greene
Jan 12, 2016·Journal of Chemical Information and Modeling·Alex Michael ClarkSean Ekins
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May 23, 2015·Journal of Chemical Information and Modeling·Alex M Clark, Sean Ekins
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Sep 8, 2016·Pharmaceutical Research·Sean Ekins
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Jul 25, 2019·Pharmaceutical Research·Sean EkinsAaron Gerlach
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Jul 3, 2019·International Journal of Molecular Sciences·Cheng-Ting ChiMax K Leong
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