Binary QSAR

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). 

Binary-QSAR. Chemical Computing Group, Montreal, Canada, 1998. 

Stahura F, Godden JW, Xue L, Bajorath J. (2000) Distinguishing between natural products and synthetic molecules by descriptor Shannon entropy analysis and binary QSAR calculations. J Chem Inf Comput Sci 40 1245-1252. 

Labute, P. (1999) Binary QSAR a new method for the determination of quantitative structure activity relationships. Pac. Symp. Biocomput., pp. 444—455. 

Gao, H. (2001) Application of BCUT metrics and genetic algorithm in binary QSAR analysis. J. Chem. Inf. Comput. Sci. 41, 402-407. 

Key Words 2D-QSAR traditional QSAR 3D-QSAR nD-QSAR 4D-QSAR receptor-independent QSAR receptor-dependent QSAR high throughput screening alignment conformation chemometrics principal components analysis partial least squares artificial neural networks support vector machines Binary-QSAR selecting QSAR descriptors. 

QSAR models created with SVMs are best utilized in Binary QSAR applications to determine if compounds are bioactive for the system of interest as is the case with high throughput screening. The Binary QSAR method of Binary-QuaSAR (7,41) of the Chemical Computing Group, Inc., is the best example of a Binary QSAR application, and is provided with the software suite MOE (28). 

An approach to scanning libraries of possible lead compounds is Binary-QSAR (7,41), a method that is based on and can be incorporated into HTS. Through the use of HTS it is possible to perform millions of physical experiments in a relatively short time span, yet there are two issues keeping it from reaching its full potential time and money (7). 

Labute, P. (2001) Binary QSAR A new technology for HTS and UHTS data analysis./. Chem. Comput. Grp.. http //www.chemcomp.com/feature/htsbqsar.htm. 

The orthogonality of a set of molecular descriptors is a very desirable property. Classification methodologies such as CART (11) (or other decision-tree methods) are not invariant to rotations of the chemistry space. Such methods may encounter difficulties with correlated descriptors (e.g., production of larger decision trees). Often, correlated descriptors necessitate the use of principal components transforms that require a set of reference data for their estimation (at worst, the transforms depend only on the data at hand and, at best, they are trained once from some larger collection of compounds). In probabilistic methodologies, such as Binary QSAR (12), approximation of statistical independence is simplified when uncorrelated descriptors are used. In addition,... 

Probabilistic methods. Other QSAR-like probabilistic approaches have also been developed for compound database mining. Binary QSAR (BQ) is discussed here as an example (Labute 1999). BQ is based on Bayes theorem of conditional probabilities ... 

An inexperienced user or sometimes even an avid practitioner of QSAR could be easily con-fiased by the multitude of methodologies and naming conventions used in QSAR studies. Two-dimensional (2D) and three-dimensional (3D) QSAR, variable selection and artificial neural network methods, comparative molecular field analysis (CoMFA), and binary QSAR present examples of various terms that may appear to describe totally independent approaches, which cannot be even compared to each other. In fact, any QSAR method can be generally defined as the application of mathematical and statistical methods to the problem of finding empirical relationships (QSARmod-els)of the form, D . D ), where... 

Dynamic QSAR (also called 4D-QSAR) denotes those recently developed SRC techniques that take conformation variability of the molecules into account [Meken-yan et al, 1994 Dimitrov and Mekenyan, 1997]. Binary QSAR refers to those techniques where attention is paid to modelling binary responses such as active/inactive compounds [Gao etal, 1999]. 

Gao et al. [36] used binary QSAR based on topological descriptors and indicator variables (including one for the phenolic hydroxyl group) to derive a classification model that separates active from inactive compounds. The model was trained on 410 diverse molecules, and it demonstrated its predictive power on a test set of 53 randomly selected molecules from which 94% were correctly classified. The biological data were selected from four different laboratories, so there might be some inconsistency with respect to the classification of the model. 

Another study on the same data set was performed by Prathipati et al. [81], They derived binary QSAR [82] models using LUDI [83,84] and MOE [85] scoring functions and obtained discriminative ability comparable with the models derived by Jacobsson [69],... 

Prathipati P, Saxena AK. Evaluation of binary qsar models derived from ludi and moe scoring functions for structure based virtual screening. J Chem Inf Model 2006 46 39-51. 

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