ArticleJournal of chemical information and modeling2008
Accurate and interpretable computational modeling of chemical mutagenicity.
Article in Journal of chemical information and modeling, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Many InChIs and quite some feat.Journal of computer-aided molecular design · 2015Article
- Integrated in silico approaches for the prediction of Ames test mutagenicity.Journal of computer-aided molecular design · 2012Article
- Open Babel: An open chemical toolbox.Journal of cheminformatics · 2011Article
- Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.Molecular diversity · 2010Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We describe a method for modeling chemical mutagenicity in terms of simple rules based on molecular features. A classification model was built using a rule-based ensemble method called RuleFit, developed by Friedman and Popescu. We show how performance compares favorably against literature methods. Performance was measured through the use of cross-validation and testing on external test sets. All data sets used are publicly available. The method automatically generated transparent rules in terms of molecular structure that agree well with known toxicology. While we have focused on chemical mutagenicity in demonstrating this method, we anticipate that it may be more generally useful in modeling other molecular properties such as other types of chemical toxicity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.