Antagonists of the human CCR5 receptor as anti-HIV-1 agents. Part 3: A proposed pharmacophore model for 1-[N-(methyl)-N-(phenylsulfonyl)amino]-2-(phenyl)-4-[4-(substituted)piperidin-1-yl]butanes

Finke, Paul E.; Meurer, Laura C.; Oates, Bryan; Shah, Shrenik K.; Loebach, Jennifer L.; Mills, Sander G.; MacCoss, Malcolm; Castonguay, Laurie A.; Malkowitz, Lorraine; Springer, Martin S.; Gould, Sandra L.; DeMartino, Julie A.

doi:10.1016/s0960-894x(01)00491-7

Cited by 41 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present paper, three different data sets have been used for the QSAR model development: (1) CCR5 binding affinity data (IC 50 ) of 119 piperidine derivatives [ 33 , 34 , 35 , 36 ]; (2) ovicidal activity data (LC 50 ) of 90 2-(2′,6′-difluorophenyl)-4-phenyl-1,3-oxazoline derivatives [ 37 ] and (3) tetrahymena toxicity (IGC 50 ) of 384 aromatic compounds [ 38 ]. For the three data sets (I, II and III), QSAR models were separately developed from genetic function approximation (GFA) technique [ 39 ] with 5,000 crossovers using Cerius2 version 4.10 software [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

On Two Novel Parameters for Validation of Predictive QSAR Models

et al. 2009

View full text Add to dashboard Cite

Validation is a crucial aspect of quantitative structure–activity relationship (QSAR) modeling. The present paper shows that traditionally used validation parameters (leave-one-out Q2 for internal validation and predictive R2 for external validation) may be supplemented with two novel parameters rm2 and Rp2 for a stricter test of validation. The parameter rm2(overall) penalizes a model for large differences between observed and predicted values of the compounds of the whole set (considering both training and test sets) while the parameter Rp2 penalizes model R2 for large differences between determination coefficient of nonrandom model and square of mean correlation coefficient of random models in case of a randomization test. Two other variants of rm2 parameter, rm2(LOO) and rm2(test), penalize a model more strictly than Q2 and R2pred respectively. Three different data sets of moderate to large size have been used to develop multiple models in order to indicate the suitability of the novel parameters in QSAR studies. The results show that in many cases the developed models could satisfy the requirements of conventional parameters (Q2 and R2pred) but fail to achieve the required values for the novel parameters rm2 and Rp2. Moreover, these parameters also help in identifying the best models from among a set of comparable models. Thus, a test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The CCR5 binding affinity data (IC 50 ) of 119 piperidine derivatives [ 33 , 34 , 35 , 36 ] were converted to logarithmic scale [pIC 50 = -logIC 50 (mM)] and then used for the QSAR study. A total of 119 compounds were selected in our study, which are shown in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

On Two Novel Parameters for Validation of Predictive QSAR Models

et al. 2009

View full text Add to dashboard Cite

show abstract

“…There were total 154 piperidine derivatives in the source papers [22][23][24][25]. 35 compounds were excluded from our study due to lack of exact numerical activity values and infrequent occurrence of particular structural features.…”

Section: Methodsmentioning

confidence: 99%

Predictive QSAR modeling of CCR5 antagonist piperidine derivatives using chemometric tools

Roy

Mandal

2008

Journal of Enzyme Inhibition and Medicinal Chemistry

View full text Add to dashboard Cite

Quantitative structure-activity relationship (QSAR) studies have been performed on piperidine derivatives (n ¼ 119) as CCR5 antagonists. The whole data set was divided into a training set (75% of the dataset) and a test set (remaining 25%) on the basis of K-means clustering technique. Models developed from the training set were used to assess the predictive potential of the models using test set compounds. Initially classical type QSAR models were developed using structural, spatial, electronic, physicochemical and/or topological parameters using statistical methods like stepwise regression, partial least squares (PLS) and factor analysis followed by multiple linear regression (FA-MLR). Using topological and structural parameters, FA-MLR provided the best equation based on internal validation (Q 2 ¼ 0.514) but the best externally validated model was obtained with PLS (R 2 pred ¼ 0.565). When structural, physicochemical, spatial and electronic descriptors were used, the best Q 2 value (0.562) was obtained from the stepwise regression derived model whereas the best R 2 pred value (0.571) came from the PLS model. When topological descriptors were used in combination with the structural, physicochemical, spatial and electronic descriptors, the best Q 2 and R 2 pred values obtained were 0.530 (stepwise regression) and 0.580 (PLS) respectively. Attempt was made to develop 3D-QSAR models using molecular shape analysis descriptors in combination with structural, physicochemical, spatial and electronic parameters. Linear models were developed using genetic function algorithm coupled with multiple linear regression. However, the results from the 3D-QSAR study were not superior to those of the classical QSAR models. Finally, artificial neural network was employed for development of nonlinear models. The ANN models showed acceptable values of squared correlation coefficient for the observed and predicted values of the test set compounds. From the view point of external predictability, selected ANN models were superior to the linear QSAR models. All reported models satisfy the criteria of external validation as recommended by Golbraikh and Tropsha (J Mol Graphics Mod 2002; 20: 269 -276), whereas the majority of the models have modified r 2 (r 2 m ) value of the test set for external validation more than 0.5 as suggested by Roy and Roy (QSAR Comb Sci 2008; 27: 302-313).

show abstract

“…This initial lead showed potent binding affinity to CCR5 (IC 50 35 nM), but only weak, micromolar activity in a PBMC viral replication assay, with a close analog also showing very low oral bioavailability in rat [40,41]. Development of SAR through a series of carbamate piperidine derivatives [42] and substituted versions thereof [43,44] led ultimately to MRK-167 2 (IC 50 0.1 nM).…”

Section: Merckmentioning

confidence: 99%