| Abstract
| - The cancer research community has begun to address the in silico modeling approaches, such as quantitativestructure−activity relationships (QSAR), as an important alternative tool for screening potentialanticancer 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, werecognized a unique opportunity to attempt to build predictive QSAR models. Here, we report a systematicevaluation of classification models to probe anticancer activity, based on linear discriminant analysisalong with 2D-molecular descriptors. This strategy afforded a final QSAR model with very good overallaccuracy and predictability on external data. Finally, we search for similarities between the naturalnucleosides, present in RNA/DNA, and the active nucleosides well-predicted by the model. The structuralinformation then gathered and the QSAR model per se shall aid in the future design of novel potent anticancernucleosides.
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