QSAR Modeling of Datasets with Enantioselective Compounds using Chirality Sensitive Molecular Descriptors

Abstract

Shape descriptors used in 3D QSAR studies naturally take into account chirality; however, for flexible and structurally diverse molecules such studies require extensive conformational searching and alignment. QSAR modeling studies of two datasets of fragrance compounds with complex stereochemistry using simple alignment-free chirality sensitive descriptors developed in our laboratories are presented. In the first investigation, 44 α-campholenic derivatives with sandalwood odor were represented as derivatives of several common structural templates  with substituents numbered according to their relative spatial positions in the molecules. Both molecular and substituent descriptors were used as independent variables in MLR calculations, and the best model was characterized by the training set q ² of 0.79 and external test set r ² of 0.95. In the second study, several types of chirality descriptors were employed in combinatorial QSAR modeling of 98 ambergris fragrance compounds. Among 28 possible combinations of seven types of descriptors and four statistical modeling techniques, k nearest neighbor classification with CoMFA descriptors was initially found to generate the best models with the internal and external accuracies of 76 and 89%, respectively. The same dataset was then studied using novel atom pair chirality descriptors (cAP). The cAP are based on a modified definition of the atomic chirality, in which the seniority of the substituents is defined by their relative partial charge values: higher values correspond to higher seniorities. The resulting models were found to have higher predictive power than those developed with CoMFA descriptors; the best model was characterized by the internal and external accuracies of 82 and 94%, respectively. The success of modeling studies using simple alignment free chirality descriptors discussed in this paper suggests that they should be applied broadly to QSAR studies of many datasets when compound stereochemistry plays an important role in defining their activity.

Keywords:

• Chirality
• Descriptors
• QSAR
• Sandalwood
• Ambergris
• Ecdysteroids

Acknowledgements

Assia Kovatcheva acknowledges the support from the DOC-program of the Austrian Academy of Science, which made possible her visit to UNC-Chapel Hill where the reported studies were conducted. This research was supported in part by the NIH research grant GM66940-01A1 awarded to Dr A. Tropsha.

Notes

Presented at the 11th International Workshop on Quantitative Structure–Activity Relationships in the Human Health and Environmental Sciences (QSAR2004), 9–13 May 2004, Liverpool, England.

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*The program Sybyl is available from Tripos Associates, St. Louis, MO.