Predictive Quantitative Structure-Activity Relationship (QSAR) models of anabolic and androgenic activities for the 17b-hydroxy-5a-androstane steroid family were obtained by means of multi-linear regression using quantum and physicochemical molecular descriptors and a genetic algorithm for the selection of the best set of descriptors. The model allows the identification, selection and future design of new steroid molecules with increased anabolic activity. Molecular descriptors included in reported models allow the structural interpretation of the biological process, evidencing the main role of the shape of molecules, hydrophobicity and electronic properties. The model for the anabolic/ androgenic ratio (expressed by the weight of the levator ani muscle and ventral prostate in mice) predicts that: a) 2-cyano-17-a-methyl-17-b-acetoxy-5a-androst-2-ene is the most potent anabolic steroid in the group and b) the testosterone-3-cyclopentenyl-enoleter is the less potent one. The approach described in this paper is an alternative for the discovery and optimization of leading anabolic compounds.

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.

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Predictive Quantitative Structure -Activity Relationship (QSAR) models of Anabolic/ Androgenic (A/A) activities for the 4,5a-dihydrotestosterone steroid family were obtained by means of multilinear regression using quantum and physicochemical Molecular Descriptors (MDs) as well as a genetic algorithm for the selection of the best subset of MDs. MDs included in our QSAR models allow the structural interpretation of the biological process, evidencing the main role of the shape of molecules, hydrophobicity, and electronic properties. Attempts were made to include lipophilicity (octanol -water partition coefficient) as well as electronic (lowest unoccupied molecular orbital properties and dipole moment) values of the whole molecules in the multivariate relations. It was found from the study that the calculated net charges by semiempirical methods of different atoms in the steroid nucleus [atoms 4 (ring A), 8 (bridgeheads of rings B and C), 11 (ring C) 13 (fusion points of rings C and D), and 16 (ring D)] contribute significantly to binding affinity. The found MDs can also be efficiently used in similarity studies based on cluster analysis. Our model for the A/A ratio (expressed by the weights of the levator ani muscle/ventral prostate in mice) predicts that 2a, 3a-difluoro-methylene-17a-methyl-5a-androstan-17b-ol (13) is the most potent anabolic steroid. By contrast, the 17a-methyl-2b, 17b-dihydroxy-5a-androstane (16) is flagged as the least potent anabolic steroid. The approach described in this report is an alternative for the discovery and optimization of leading anabolic compounds among steroids and analogues. It also gives an important role to electron exchange terms of molecular interactions to this kind of steroid activity.

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