2D QSAR models

In an independent study, Yoshida and Niwa [20] analyzed a larger and more diverse set of molecules (104 compounds) and developed a 2D QSAR model, which gave results similar to that of Cronin [19] but added some more details with regard to the physicochemical properties involved in the hERG blockade by drugs. Equation 5.2 represents the best model ... 

The relevance of size-related properties of hERG-blocking molecules was also detected in a 2D QSAR model developed by Coi et al. [22] after the analysis of 82 compounds through the CODESSA method. These authors developed two multiparameter models with strong predictive properties, from which, besides the involvement of hydrophobic features, the importance of linearity as opposed to globularity of the hERG blockers emerged. 

In Table 5.1, we present a list of the main physicochemical and structural properties associated with the descriptors included in the 2D QSAR models discussed above. Of course, we did some generalizations in an attempt to refer different parameters and descriptors to the same property, but the effort was devoted at identifying the smallest number of significant features positively or negatively correlated to the hERG blockade by small molecules. Examining the properties... 

Table 5.1 Molecular properties identified as relevant in 2D QSAR models of hERG blockade by small molecules.

Hoffman, B.T., Kopajtic, T., Katz, J.L., and Newman, A.H. 2D QSAR Modeling and preliminary database searching for dopamine transporter inhibitors using genetic algorithm variable selection of Molconn Z descriptors./. Med. Chem. 

In 2D-QSAR, model building is based on 2D representation of molecules and 2D descriptors. However, it has become very common to generate 3D-QSAR models. 

D-QSAR models based on multiple linear regression (MLR) are easy to understand, especially when interpretable descriptors are used. MLR is not as powerful as more complicated methods, such as neural networks, so careful formulation of descriptors is critical for the derivation of models that are both predictive and practically useful. Here is a set of guidelines that we have found to be useful in the formulation of descriptors for interpretable models ... 

Using carefully selected descriptors, Yoshida et al. [16] constructed simple and easy-to-interpret 2D-QSAR models for hERG inhibition ... 

Several series of novel chirality descriptors of chemical organic molecules were introduced by Golbraikh et al. [5, 6]. These descriptors have been implemented in a QSAR study with a high content of chiral and enantiomeric compounds. It was shown fhat for all data sets 2D-QSAR models that use a combination of chirahty descriptors wifh conventional topological descriptors afford better or similar predictive abihty when compared to models generated wifh 3D-QSAR approaches. 2D-QSAR mefhods enhanced by chirahty descriptors present a powerful alternative to popular 3D-QSAR approaches. 

D QSAR Models Hansch and Free-Wilson Analyses... 

D QSAR Modeling and Preliminary Database Searching for Dopamine Transporter Inhibitors Using Genetic Algorithm Variable Selection of Molconn Z Descriptors. 

The metabolic lability in human liver microsomes was predicted with a 2D-QSAR model. 

The 2D-QSAR models presented in this work were mostly developed using multiple linear regression (MLR) analysis. However, the 3D-QSAR models were developed using various techniques such as CoMFA, CoMSIA, pharmacophore generation, free-energy binding analysis and many others. A note has been made along with the QSAR discussion about the technique used. 

QSAR 8 mentioned earlier was derived for the same dataset [75]. Ionization potential (IP), ClogP, molar volume (as MV/lOO) and an indicator variable (1) were used to model the activity. A parabolic model with ClogP unfolded the ideal ClogP value (ClogPo = 5.697) for maximum anti-protease activity. It is of note that the activities of the test set compounds (total eight) were predicted well both by the 2D-QSAR model (r = 0.746) of Wilkerson et al. [75] and the CoMFA model (r = 0.971) of Debnath [99]. 

Di Santo et al. [181] also conducted CoMFA studies for the same dataset for which the 2D-QSAR model was developed (QSAR 66). A cross-validated model with two principal component (r = 0.607) and a non-cross validated model with of 0.869 were obtained. The model showed a major contribution of steric fields (56.7%) over electrostatic fields (43.3%), which was in agreement with the 2D-QSAR model The 3D-QSAR models were able to predict the anti-protease activity but were unable to discriminate anti-protease active compounds from the inactive compounds. The model also predicted the biological activity of a set of 20 newly synthesized arylpropanolamines and indicated their binding mode to be similar to the diarylbutanols used to derive the CoMFA model... 

---