title: Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set.
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.
authors.affiliation: REQUIMTE and CIQ, Department of Chemistry, University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal.
authors.forename: Aliuska Morales
authors.initials: AM
authors.lastname: Helguera
authors.forename: J E
authors.initials: JE
authors.lastname: Rodríguez-Borges
authors.forename: Xerardo
authors.initials: X
authors.lastname: García-Mera
authors.forename: Franco
authors.initials: F
authors.lastname: Fernández
authors.forename: M Natália D S
authors.initials: MN
authors.lastname: Cordeiro
mesh_terms: 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
chemical_list: D000970:Antineoplastic Agents; D009705:Nucleosides