Tanimoto models

In the NN method, the property F of the target compound is calculated as an average (or weighted average) of that for its NN in the space of descriptors selected for the model. Different metrics (Euclidian distances, Tanimoto similarity coefficients, etc.), can be used to identify the neighbors. Their number k is optimized using a cross-validation procedure for the training set. 

In order to establish whether a query chemical compound fits within the applicability domain of the QSAR model (i.e., that the training set for the model contained molecules chemically similar to the query molecule), the similarity of the uploaded structure (and its predicted metabolites if the metabolite QSAR prediction option is selected) to the structures used in the training set for the QSAR model can be calculated (maximal Tanimoto coefficient (15)). The higher the similarity value, the greater is the applicability of the model. Results of QSAR modeling for... 

Fig. 5. Subset of possible targets for clozapine (only targets classed as enzymes are shown) showing the predicted target, the database compound driving the prediction, the Tanimoto coefficient percentage of its similarity to the query compound (in this case clozapine was found in the database, hence similarity score is 100%), the effect on the target (where known) and the PubMed ID or patent ID from which the compound-target association was drawn. Targets predicted by QSAR models are also indicated.

The agreement degree between experimental and model diagrams is quite satisfactory (S(E,M) = 89.4). The Tanimoto indices have been calculated (Sorensen et al. 2003) ... 

Tanimoto coefficients between the seven hit structures and the most similar analogues were calculated on the basis of MACCS keys and graph-3-point pharmacophore fingerprints. The actual docking rank from the docking of the 320 Maybridge compounds that passed the pharmacophore model is indicated. 

Tanimoto, H., Lau, K.H., Nishimoto, S.K., Wergedal, J.E., and Baylink, D.J. (1991) Evaluation of the Usefulness of Serum Phosphatases and Osteocalcin as Serum Markers in a Calcium Depletion-Repletion Rat Model, Calcif. Tissue Int. 48, 101-110. 

Our results demonstrate the effectiveness of the suggested methodology. Application of 1-SVM models to virtual screening can be viewed as a special kind of similarity search, in which similarity is considered with respect not to individual actives but to the whole dataset of active compounds. Being an alternative to the traditional similarity search for active compounds using of the Tanimoto coefficient and to the binary classification methods, this approach possesses unique properties. In contrast to the binary classification methods, the described method is not sensitive to the choice of counterexamples. Moreover, with default values of parameters,... 

A molecular similarity kernel, the Tanimoto similarity kernel, was used by Lind and Maltseva in SVM regression to predict the aqueous solubility of three sets of organic compounds. The Taniomto similarity kernel was computed from molecular fingerprints. The RMSE and q cross-validation statistics for the three sets show a good performance of SVMR with the Tanimoto kernel set 1 (883 compounds), RMSE = 0.62 and q = 0.88 set 2 (412 compounds), RMSE = 0.77 and = 0.86 and set 3 (411 compounds), RMSE = 0.57 and 17 = 0.88. An SVMR model was trained on set 1 and then tested on set 2 with good results, i.e., RMSE = 0.68 and q = 0.89. 

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