Probing the anticancer activity of nucleoside analogues: A QSAR model approach using an internally consistent training set

• Type: Article in International Scientific Journal
• Date: 2007

Authors

Aliuska Morales Helguera (Author) Other; Rodriguez Borges, JE (Author) FCUP; Xerardo Garcia Mera (Author) Other; Franco Fernandez (Author) Other; Natalia, M (Author) FCUP; Cordeiro, DS (Author) Other

Journal

Title: Journal of Medicinal Chemistry

Vol. 50

Pages: 1537-1545

ISSN: 0022-2623

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 33 Citations

Scopus - 37 Citations

Scientific classification

FOS: Medical and Health sciences > Basic medicine

Other information

Authenticus ID: P-004-ANT

DOI: 10.1021/jm061445m

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9