Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10822-015-9829-4 | Journal of computer-aided molecular design |
Keywords | Field | DocType |
Lipophilicity,PLS,SVR,VolSurf+ descriptors,Peptides | Computational chemistry,Chemistry,Lipophilicity,Intermolecular force | Journal |
Volume | Issue | ISSN |
29 | 4 | 1573-4951 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessia Visconti | 1 | 0 | 0.34 |
| Giuseppe Ermondi | 2 | 4 | 1.39 |
| Giulia Caron | 3 | 4 | 1.39 |
| Roberto Esposito | 4 | 64 | 10.87 |