Peptoid positional scanning libraries for identification of multidrug resistance reversal agents

Peptide-peptoid hybrids based on (1-11)-parathyroid hormone analogs

A series of peptide-peptoid hybrids, containing N-substituted glycines, were synthesized based on the H-Aib-Val-Aib-Glu-Ile-Gln-Leu-Nle-His-Gln-Har-NH(2) (Har = Homoarginine) as the parent parathyroid hormone (1-11) analog. The compounds were pharmacologically characterized in their agonistic activity at the parathyroid hormone 1 receptor.

Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors

AIM

To develop an artificial neural network model for predicting the resistance index (RI) of taxoids.

METHODS

A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state (E-state) indices were calculated to characterize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models.

RESULTS

The final model proved to be good with the cross-validation Q2cv0.62, external testing R2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933.

CONCLUSION

The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.