NDLI: Prediction and interpretation of the lipophilicity of small peptides

Content Provider	Springer Nature Link
Author	Visconti, Alessia Ermondi, Giuseppe Caron, Giulia Esposito, Roberto
Copyright Year	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 $$\log D_{\text {oct}}$$ 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.
Starting Page	361
Ending Page	370
Page Count	10
File Format	PDF
ISSN	0920654X
Journal	Journal of Computer-Aided Molecular Design
Volume Number	29
Issue Number	4
e-ISSN	15734951
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-01-11
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Lipophilicity PLS SVR VolSurf+ descriptors Peptides Physical Chemistry Computer Applications in Chemistry Animal Anatomy / Morphology / Histology
Content Type	Text
Resource Type	Article
Subject	Drug Discovery Physical and Theoretical Chemistry Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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