Combinatorial QSAR of Ambergris Fragrance Compounds

Abstract

A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.

In silico ligand based design of indolylpiperidinyl derivatives as novel histamine H1 receptor antagonists

“…The data set consisting of compounds with molecular structures and their biological activities were divided into a training and test set. Twenty percent of the compounds were selected with a maximum dissimilarity algorithm and assigned to the test set; with the remaining 80% assigned to the training set (18). The training set comprised of 41 molecules was used for QSAR model development and the test set of 10 molecules was used for model validation.…”

Computer-aided aroma design. II. Quantitative structure–odour relationship

“…Those charts, also found in wine tasting industry enable to set a standard description of odours but subtle differences may arise from one author to another, especially when considering odour secondary notes. Evaluation of the odour note of a novel molecule is then a key issue that has been attempted for several primary notes through the use of Structure -Odour Relationships (SOR): for camphor (Chastrette et al, 1996), for sandalwood (Zakarya et al, 1997), for musk (Cherqaoui et al, 1998) and for ambergris (Kovatcheva et al, 2004).…”

Hierarchical QSAR technology based on the Simplex representation of molecular structure

“…Different QSAR (quantitative structureactivity relationship) approaches are used in this case. For many years, QSAR has been successfully used to analyze a large variety of parameters, including antiviral and anticancer activity, toxicity, among others [3][4][5][6][7][8][9][10][11][12][13][14]. Its staying power may be attributed to the strength of its initial postulate, which is that activity is a function of structure, and the rapid and extensive development of the methodology and computational techniques.…”