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Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set
- Source :
- Journal of medicinal chemistry. 50(7)
- Publication Year :
- 2007
-
Abstract
- The cancer research community has begun to address the in silico modeling approaches, such as quantitative structure-activity relationships (QSAR), as an important alternative tool for screening potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Here, we report a systematic evaluation of classification models to probe anticancer activity, based on linear discriminant analysis along with 2D-molecular descriptors. This strategy afforded a final QSAR model with very good overall accuracy and predictability on external data. Finally, we search for similarities between the natural nucleosides, present in RNA/DNA, and the active nucleosides well-predicted by the model. The structural information then gathered and the QSAR model per se shall aid in the future design of novel potent anticancer nucleosides.
- Subjects :
- Models, Molecular
Quantitative structure–activity relationship
Training set
Molecular model
Databases, Factual
Stereochemistry
Chemistry
In silico
Discriminant Analysis
Quantitative Structure-Activity Relationship
Antineoplastic Agents
Nucleosides
Computational biology
Linear discriminant analysis
External data
Drug Discovery
Molecular Medicine
Nucleoside
Algorithms
Subjects
Details
- ISSN :
- 00222623
- Volume :
- 50
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- Journal of medicinal chemistry
- Accession number :
- edsair.doi.dedup.....1852dbb19bca941316f157615c8efa64