Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict logDoct of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict logDoct of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.

Prediction and interpretation of the lipophilicity of small peptides

VISCONTI, ALESSIA;ERMONDI, Giuseppe
;
CARON, Giulia;ESPOSITO, Roberto
2015

Abstract

Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict logDoct of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict logDoct of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.
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http://link.springer.com/article/10.1007%2Fs10822-015-9829-4
Lipophilicity, PLS, SVR, VolSurf+ descriptors, Peptides
A. Visconti; G. Ermondi; G. Caron; R. Esposito
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1508110
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