Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

Yoanna María Alvarez-Ginarte, Yovani Marrero-Ponce, José Alberto Ruiz-GarcíA, Luis Alberto Montero-Cabrera, Jose Manuel García De La Vega, Pedro Noheda Marin, Rachel Crespo-Otero, Francisco Torrens Zaragoza, Ramón García-Domenech

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

The great cost associated with the development of new anabolic-androgenic steroid (AASs) makes necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, quantum, and physicochemical molecular descriptors, plus linear discriminant analysis (LDA) were used to analyze the anabolic/androgenic activity of structurally diverse steroids and to discover novel AASs, as well as also to give a structural interpretation of their anabolic-androgenic ratio (AAR). The obtained models are able to correctly classify 91.67% (86.27%) of the AASs in the training (test) sets, respectively. The results of predictions on the 10% full-out cross-validation test also evidence the robustness of the obtained model. Moreover, these classification functions are applied to an "in house" library of chemicals, to find novel AASs. Two new AASs are synthesized and tested for in vivo activity. Although both AASs are less active than some commercially AASs, this result leaves a door open to a virtual variational study of the structure of the two compounds, to improve their biological activity. The LDA-assisted QSAR models presented here, could significantly reduce the number of synthesized and tested AASs, as well as could increase the chance of finding new chemical entities with higher AAR.

Original languageEnglish
Pages (from-to)317-333
Number of pages17
JournalJournal of Computational Chemistry
Volume29
Issue number3
DOIs
StatePublished - Feb 2008
Externally publishedYes

Keywords

  • Anabolic-androgenic ratio
  • Anabolic-androgenic steroid
  • LDA-assisted QSAR model
  • Quantum and physicochemical molecular descriptor
  • Virtual screening

Fingerprint

Dive into the research topics of 'Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids'. Together they form a unique fingerprint.

Cite this