Combinatorial QSAR

Numerous QSAR tools have been developed [152, 154] and used in modeling physicochemical data. These vary from simple linear to more complex nonlinear models, as well as classification models. A popular approach more recently became the construction of consensus or ensemble models ( combinatorial QSAR ) combining the predictions of several individual approaches [155]. Or, alternatively, models can be built by rurming the same approach, such as a neural network of a decision tree, many times and combining the output into a single prediction. 

Combinatorial QSAR modeling of P-glycoprotein substrates. J. Chem. Inf. Model. 2006, 46, 1245-1254. 

Cabrera et al. [50] modeled a set of 163 drugs using TOPS-MODE descriptors with a linear discriminant model to predict p-glycoprotein efflux. Model accuracy was 81% for the training set and 77.5% for a validation set of 40 molecules. A "combinatorial QSAR" approach was used by de Lima et al. [51] to test multiple model types (kNN, decision tree, binary QSAR, SVM) with multiple descriptor sets from various software packages (MolconnZ, Atom Pair, VoSurf, MOE) for the prediction of p-glycoprotein substrates for a dataset of 192 molecules. Best overall performance on a test set of 51 molecules was achieved with an SVM and AP or VolSurf descriptors (81% accuracy each). 

Validated QSAR Modelingasan Empirical Data-modelingApproach Combinatorial QSAR 443... 

Validated QSAR Modeling as an Empirical Data-modeling Approach Combinatorial QSAR... 

P., Bughbauer, G., Tropsha, A. Combinatorial QSAR of ambergris fragrance compounds./. Chem. Inf. Comput. Sci. 2004, 44, 582-595. 

Zhu, H., Tropsha, A., Fourches, D., Varnek, A., Papa, E., Gramatica, P., Oberg, T., Dao, P., Cherkasov, A. and Tetko, I.V. (2008) Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. Journal of Chemical Information and Computer Sciences, 48, 766-784. 

Kovatcheva A, Golbraikh A, Oloff S, Xiao YD, Zheng W, Wolschann P, et al. A combinatorial QSAR of Ambergris fragrance compounds. J Chem Inf Comput Sci 2004 44 582-95. 

De Cerqueira Lima, P., Golbraikh, A., OlofF, S., Xiao, Y. and Tropsha, A. (2006) Combinatorial QSAR modelling of P-glycoprotein substrates. Journal of Chemical Information and Modeling, 46, 1245-1254. 

One of the problems when applying classical QSAR techniques is the right choice of the method and the descriptor combination. In principle, two general approaches might be undertaken to overcome this issue, which normally is pursued on a trial and error basis. One is to automatically combine feature selection algorithms with classification and regression tools and the other is to combinatorially explore the descriptor/method space. The latter was recently introduced by the group of Tropsha (combinatorial QSAR) [47]. 

In a typical QSAR publication it is now more usual to see some attempt to compare modelling methods against one or more datasets.Some workers have developed automated systems to do this, describing it as combi or combinatorial QSAR. ... 

Solkneo R, Zhang J, Kim M, Sedykh A, Zhu H (2012) Predicting chemical ocular toxicity using a combinatorial QSAR approach. Chemical Research in Toxicology 25 2763-2769. 

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