Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set.
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.
Helguera
Aliuska Morales
AM
REQUIMTE and CIQ, Department of Chemistry, University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal.
Rodríguez-Borges
J E
JE
García-Mera
Xerardo
X
Fernández
Franco
F
Cordeiro
M Natália D S
MN
D000465:Algorithms; D000970:Antineoplastic Agents / Q000737:chemistry*; D016208:Databases, Factual; D016002:Discriminant Analysis; D008958:Models, Molecular; D009705:Nucleosides / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
D000970:Antineoplastic Agents; D009705:Nucleosides