title: Prediction and interpretation of the lipophilicity of small peptides.
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 [Formula: see text] 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 [Formula: see text] 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.
authors.affiliation: Department of Genomics of Common Disease, Imperial College London, Du Cane Road, W12 ONN, London, UK, a.visconti@imperial.ac.uk.
authors.forename: Alessia
authors.initials: A
authors.lastname: Visconti
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Ermondi
authors.forename: Giulia
authors.initials: G
authors.lastname: Caron
authors.forename: Roberto
authors.initials: R
authors.lastname: Esposito
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015195:Drug Design; D057927:Hydrophobic and Hydrophilic Interactions; D008055:Lipids / Q000737:chemistry; D008956:Models, Chemical; D010455:Peptides / Q000737:chemistry*
chemical_list: D008055:Lipids; D010455:Peptides