ArticleJournal of molecular modeling2006
A radial-distribution-function approach for predicting rodent carcinogenicity.
Article in Journal of molecular modeling, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Identification of Metabotropic Glutamate Receptor Subtype 5 Potentiators Using Virtual High-Throughput Screening.ACS chemical neuroscience · 2010Article
- Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices.Journal of theoretical biology · 2009Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Carcinogenic activity has been investigated using the Radial-Distribution-Function (RDF) approach. A discriminant model was developed to predict the carcinogenic and non-carcinogenic activity on a data set of 188 compounds. The percentage of overall classification was 76.4% for the carcinogenic chemicals and 72.5% for the non-carcinogenic chemicals. The predictive power of the model was validated by two tests: a cross-validation by the resubstitution technique and a test set (compounds not used in the development of the model) with 79.3 and 72.5% good classification, respectively. The RDF descriptors were compared with eight other methodologies; Constitutional, Molecular walks counts, Galvez topological charge indices, 2D autocorrelations, Randić molecular profiles, Geometrical, 3D-MORSE, and WHIM, demonstrating that the RDF descriptors are better to the rest of the approaches used.
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Registered trials
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