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Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids
Author(s) -
AlvarezGinarte Yoanna María,
MarreroPonce Yovani,
RuizGarcía José Alberto,
MonteroCabrera Luis Alberto,
García De La Vega Jose Manuel,
Noheda Marin Pedro,
CrespoOtero Rachel,
Zaragoza Francisco Torrens,
GarcíaDomenech Ramón
Publication year - 2008
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20745
Subject(s) - chemistry , robustness (evolution) , quantum chemical , quantitative structure–activity relationship , virtual screening , artificial intelligence , computational chemistry , computer science , combinatorial chemistry , stereochemistry , molecule , molecular dynamics , biochemistry , organic chemistry , gene
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. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008