Target and Antitarget Pharmacophore Modeling

Size and shape descriptors confirmed their effectiveness in correlating with the binding affinities. In fact, the most significant QSAR models obtained involved Vdif, in the case of the 5-HT1AR, and Vou, or Vdif f°r the oq-AR  

A significant improvement in the correlations was obtained by combining size/shape descriptors with MO indexes in multilinear regression models  

Finally, a large variety of QSAR models of 5-HT1A-R/a -AR target/antitarget selectivity were also obtained. The following equation shows the model with best statistics  

A classical Hansch approach and an artificial neural networks approach were applied to a training set of 32 substituted phenylpiperazines characterized by their affinity for the 5-HTiA-R and the generic arAR [91]. The study was aimed at evaluating the structural requirements for the 5-HTiA/ai selectivity. Each chemical structure was described by six physicochemical parameters and three indicator variables. As electronic descriptors, the field and resonance constants of Swain and Lupton were used. Furthermore, the vdW volumes were employed as steric parameters. The hydrophobic effects exerted by the ortho- and meta-substituents were measured by using the Hansch 7t-ortho and n-meta constants [91]. The resulting models provided a significant correlation of electronic, steric and hydro-phobic parameters with the biological affinities. Moreover, it was inferred that the 

5-HT1A receptor is able to accommodate bulky substituents in its active site, whereas the steric requirements of the oq-AR at this position are more restricted [91]. 

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