Modeling drug albumin binding affinity with E-State topological structure representation

The binding affinity to human serum albumin for 94 drugs was modeled with topological descriptors of molecular structure, using as experimental data the HPLC chromatographic retention index [logk(HSA)] on immobilized albumin. The electrotopological state (E-State) along with the molecular connectivity chi indices provided the basis for a satisfactory model: r(2) = 0.77, s = 0.29, q(2) = 0.70, s(press) = 0.33. The 10% leave-group-out (LGO) cross-validation method yielded q(2) (= r(press)(2)) = 0.69. Further, the model was tested on a 10 compound external validation set, yielding a mean absolute error, MAE = 0.31; q(2) (= r(press)(2)) = 0.74. MDL QSAR software was used for setting up the data set, creation of combination descriptors, modeling, and database management. All the statistical tests indicate that the topological model is useful for property estimation. Internal and external validation methods were used, and the results indicate that the model is useful for prediction. Randomizations of the activity values also indicate statistically sound models are very different from random statistics. The model indicates that positive factors for binding affinity include electron accessibility and the number of aromatic rings, aliphatic CH groups (-CH3, -CH2-, >CH-), halogens (fluorine and chlorine), and -OH groups. Five-membered heteroatomic rings present a negative factor, whereas six-membered heteroatomic rings present a positive factor. The specific information described can be used as an aid to the drug design process.