Predictive CoMFA models

Additionally, computational chemists often use the resulting output alignment of the molecules as input for 3D-QSAR modeling. As already stated, most field-based 3D-QSAR approaches (such as CoMFA) need a pre-aligned set of molecules and the pharmacophore method is certainly one of the best ways to obtain an objective alignment of the compounds. Klabunde et al., for instance, have recently reported the use of a pharmacophore model of human liver glycogen phosphorylase inhibitors together with 3D information from inhibitor-enzyme complexes to derive a predictive CoMFA model [98]. 

Ungwitayatorn et al. [240] reported the 3D-QSAR CoMFA/CoMSIA studies for a series of 30 Chromone derivatives of HIVPI. The dataset was divided into a training/test set of 30/5 compounds based on the distribution of biological activity and the variety of substitution pattern. Superposition and field fit alignment criteria were used for model development in CoMFA. The best predictive CoMFA model with steric (46%) and electrostatic (54%) fields gave cross-validated of 0.763 and non-cross-validated of 0.967 with a standard error of estimate (S) of 5.092. The PLS protocol and stepwise procedure were... 

Is 3D-QSAR best left to experts, or can less skilled scientists apply the methods The discussions above may indmidate a nonexpert who had contemplated trying 3D-QSAR methods. In fact, the common approaches such as CoMFA are easily learned and hard to misuse. A little time spent learning how to interpret PLS statistics is all that is needed to supplement the individual s molecular modeling experience. If problems in alignment, choice of conformation, calculation of properties, or suspected nonlinearities arise, an expert collaborator should be sought. In our experience, most data sets fairly easily yield predictive CoMFA models, and those that do not often fail to improve with additional changing of parameters. 

A preliminary CoMFA model for a structurally highly diverse series is needed to estimate lipophilicity parameters of new compounds. So far, no heterogeneous series has been reported that could lead to a general predictive CoMFA model for log P calculations. 

Gomplex field-based 3-D QSAR models have also been applied to the problem of predicting hERG activity. Gavalli ef al. [85] used a CoMFA model, as previously discussed. Pearlstein ef al. [89] modeled a set of sertindole analogs using compara-... 

To test the applicability of this approach, eight compounds were randomly removed. The remaining 52 compounds were used to develop a CoMFA model. The resultant model was then used to predict the solubility of the removed compounds. Results (Table 3.7) show that predicted values agree well with the experimental values. 

The q2 value of a CoMFA model, together with other statistical information from the pis analysis, provides information on the predictive capability of the model. In this study we have generated CoMFA models that describe the pharmacophore either with or without the involvement of hemin, both of which provide good q2 values. Selection of the model that most accurately depicts reality is not trivial since many variables are inherent in the cell-culture bioassay results. However, it may be... 

We chose to design and then synthesize a class of compounds heretofore unknown in the artemisinin area 8,8-disubstituted-D-norartemisinins. While the parent molecule 421 was not predicted in the CoMFA models to be highly active, homologues of 421 were.155... 

The predictions from the achiral CoMFA model 199 (2 A/C.3), which includes the racemates, best reflects experimentally derived antimalarial activities. The predicted value for the natural enantiomer of 421 was 110% of artemisinin and 12%... 

Table 20. Prediction Values for 421 and 422 in Different CoMFA Models...

In 1993, Martin and colleagues performed a follow-up study on the same 15 compounds using CoMFA. CoMFA does not produce a simple equation in the manner of Hansch analysis, but CoMFA does calculate activities for the training set compounds. Comparing the predicted and experimental activities allows determination of the model s correlation coefficient. The CoMFA performed by Martin gave an r-value of 0.96. Martin s CoMFA model accounted... 

Martin s expanded CoMFA model gave an r-value of 0.92. Six new analogues of 12.18 were prepared, and their activities were predicted with the new model. The RMS of the experimental and calculated activity differences was only 0.36. 

Amin EA, Welsh WJ (2006) Highly predictive CoMFA and CoMSIA models for two series of stromelysin-1 (MMP-3) inhibitors elucidate SI and S1-S2 binding modes. J Chem Inf Model 46 1775-1783... 

Ring-substituted quinolines 70 CoMFA and CoMSIA Tested with 24 molecules. With the CoMFA model R2 = 0.42. 18 molecules were suggested for synthesis based on the CoMFA predictions Nayyar et al. (45)... 

Table 13.2 Predictions from the CoMFA Model for the Data from Kuiper et al. (1997 1998)... 

In contrast, a CoMFA model based on a structurally diverse data set provides more robust predictions. However, the critical and most difficult part of developing a CoMFA model, such as... 

Figure 13.11 Overview diagram of the NCTR Four-Phase approach for priority setting. In Phase I, chemicals with molecular weight < 94 or > 1000 or containing no ring structure will be rejected. In Phase II, three approaches (structural alerts, pharmacophores, and classification methods) that include a total of 11 models are used to make a qualitative activity prediction. In Phase III, a 3D QSAR/CoMFA model is used to make a more accurate quantitative activity prediction. In Phase IV, an expert system is expected to make a decision on priority setting based on a set of rules. Different phases are hierarchical different methods within each phase are complementary.

In this Phase, the CoMFA model (Section IV.E) was used to make a more accurate quantitative activity prediction for chemicals from Phase II. Chemicals with higher predicted binding affinity are given higher priority for further evaluation in Phase IV. The CoMFA model demonstrated good statistical reliability in both cross-validation and external validation (Shi et al., 2001). 

Table 13.5 summarizes the priority setting results for the two data sets using the NCTR Four-Phase system. When only the Phase I and II protocols are used, the system dramatically reduced the number of potential estrogens by some 80 to 85%, demonstrating its effectiveness in eliminating these most unlikely ER binders from further expensive experimentation. The Phase III CoMFA model further reduces the data size by about 5 to 10%. More importantly, the quantitative binding affinity prediction from Phase III provides an important rank-order value for priority setting. 

In addition to the steric and electrostatic descriptors, it was proposed to use other 3D molecular fields characterized by the sampling over the rectangular grid - in particular, the hydrophobic field/molecular lipophilic potential (MLP), ° hydrogen bonding and quantum-chemical parameters, e.g., orbital densities.Descriptor selection techniques are often recommended to enhance the stability, predictivity and interpretability of the CoMFA models. ... 

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