Journal of Computer-Aided Molecular Design

, Volume 29, Issue 4, pp 361–370 | Cite as

Prediction and interpretation of the lipophilicity of small peptides

  • Alessia Visconti
  • Giuseppe ErmondiEmail author
  • Giulia Caron
  • Roberto Esposito


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 \(\log D_{\text {oct}}\) 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 \(\log D_{\text {oct}}\) 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.


Lipophilicity PLS SVR VolSurf+ descriptors Peptides 



This work has been supported by Ateneo Compagnia di San Paolo-2012-Call 2, LIMPET project.

Supplementary material

10822_2015_9829_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (pdf 1157 KB)


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alessia Visconti
    • 1
  • Giuseppe Ermondi
    • 2
    Email author
  • Giulia Caron
    • 2
  • Roberto Esposito
    • 3
  1. 1.Department of Genomics of Common DiseaseImperial College LondonLondonUK
  2. 2.Molecular Biotechnology and Health Sciences DepartmentUniversity of TorinoTurinItaly
  3. 3.Department of Computer ScienceUniversity of TorinoTurinItaly

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