QSAR Modeling

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. 

In modern drug discovery speed and cost control are important in addition to high quality. In silica virtual screening for drugability [159] is a good first step in library 

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Hopfinger et al. [53, 54] have constructed 3D-QSAR models with the 4D-QSAR analysis formahsm. This formalism allows both conformational flexibility and freedom of alignment by ensemble averaging, i.e., the fourth dimension is the dimension of ensemble sampling. The 4D-QSAR analysis can be seen as the evolution of Molecular Shape Analysis [55, 56]. 

Activity prediction is based on a list of models (i.e., QSAR models, pharmacophore models, etc.) that are maintained on the server. There is a second level of access so that only authorized users may be allowed to add or delete model entries. 

The information in the structures and known activity data is good enough to create a QSAR model with a confidence of 75% ... 

The variable selection methods have been also adopted for region selection in the area of 3D QSAR. For example, GOLPE [31] was developed with chemometric principles and q2-GRS [32] was developed based on independent CoMFA analyses of small areas of near-molecular space to address the issue of optimal region selection in CoMFA analysis. Both of these methods have been shown to improve the QSAR models compared to original CoMFA technique. 

Shen M, Beguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A. Application of predictive QSAR models to database mining identification and experimental validation of novel anticonvulsant compounds. J Med Chem 2004 47(9) 2356-64. 

Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 2003 43(6) 1947-58. 

Kovatdieva A, Golbraikh A, Oloff S, Feng J, Zheng W, Tropsha A. QSAR modeling of datasets with enantioselective compounds using chirality sensitive molecular descriptors. SAR QSAR Environ Re. 2005 16(l-2) 93-102. 

MetaDrug Metabolism database. Metabolite prediction. Metabolite prioritization, QSAR models for enzymes, transporters and network building algorithms for Systems-ADME/Tox www.genego.com... 

QSAR modeling. Therefore considerably larger and more consistent data sets for each enzyme will be required in future to increase the predictive scope of such models. The evaluation of any rule-based metabolite software with a diverse array of molecules will indicate that it is possible to generate many more metabolites than have been identified in the literature for the respective molecules to date, which could also reflect the sensitivity of analytical methods at the time of publishing the data. In such cases, efficient machine learning algorithms will be necessary to indicate which of the metabolites are relevant and will be likely to be observed under the given experimental conditions. 

Livingstone [24] has given a number of recommendations for successful QSAR modeling ... 

MLR is the most widely used of the QSAR modeling techniques. Walker et al. [15] have published guidelines for the development and use of MLR-based QSARs, and Cronin and Schultz [41] have discussed their potential pitfalls. 

A QSAR for which the standard error of each descriptor is given concerns the bradycardic effect of a series of tetraalkylbispidines [47]. The QSAR models the selectivity between the desired bradycardic effect and the adverse contractile effect. It is important, in assessing and modeling drug toxicity, that the toxic effect is assessed relative to the desired effect as described above. The QSAR developed for the selectivity of the tetraalkylbispidines was ... 

A key requirement of QSAR is that the compounds used in the modeling and prediction processes should have the same mechanism of action, and for this reason most QSAR studies are made with congeneric series of compounds. However, if a diverse set of compounds can reasonably be assumed to have the same mechanism of action, QSAR modeling can justihably be carried out. For example, Dearden et al. [43] developed a QSAR for the ratio of brain levels of 22 very diverse drugs in the wild-type mouse and the P-glycoprotein knockout mouse (R+/ ) ... 

So far, we have considered the QSAR modeling of continuous biological data, that is, where the toxicity value is a number such as an LD50. However, some data are not continuous but are binary (e.g., toxic/ nontoxic) a common example is carcinogenicity, for which test results are almost invariably reported in this way. Clearly, one cannot perform, say, MLR on such classification data (although a method called fuzzy adaptive least squares [70] can be used). A number of methods are available for the modeling of classification data. 

Livingstone DJ. Building QSAR models a practical guide. In Cronin MTD, Livingstone DJ, editors. Predicting chemical toxicity and fate. Boca Raton CRC Press, 2004. p. 151-70. 

Dearden JC, Netzeva TI. QSAR modelling of hERG potassium channel inhibition with low-dimensional descriptors. I Pharm Pharmacol 2004 56 Suppl S-82. 

Devillers J. A general QSAR model for predicting the acute toxicity of pesticides to Lepomis macrochirus. SAR QSAR Environ Res 2001 11 397-417. 

The Danish EPA has developed an advisory list for self-classification of dangerous substances including 20 624 substances. The substances have been identified by means of QSAR models (Quantitative Structure-Activity Relationship) as having acute oral toxicity, sensitization, mutagenicity, carcinogenicity, and/or danger to the aquatic environment. 

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