Issue 9, 2018

Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning

Abstract

Lipophilicity prediction is routinely applied to small molecules and presents a working alternative to experimental log P or log D determination. For compounds outside the domain of classical medicinal chemistry these predictions lack accuracy, advocating the development of bespoke in silico approaches. Peptides and their derivatives and mimetics fill the structural gap between small synthetic drugs and genetically engineered macromolecules. Here, we present a data-driven machine learning method for peptide log D7.4 prediction. A model for estimating the lipophilicity of short linear peptides consisting of natural amino acids was developed. In a prospective test, we obtained accurate predictions for a set of newly synthesized linear tri- to hexapeptides. Further model development focused on more complex peptide mimetics from the AstraZeneca compound collection. The results obtained demonstrate the applicability of the new prediction model to peptides and peptide derivatives in a log D7.4 range of approximately −3 to 5, with superior accuracy to established lipophilicity models for small molecules.

Graphical abstract: Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning

Supplementary files

Article information

Article type
Research Article
Submitted
23 Jul 2018
Accepted
07 Aug 2018
First published
22 Aug 2018

Med. Chem. Commun., 2018,9, 1538-1546

Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning

J. Fuchs, F. Grisoni, M. Kossenjans, J. A. Hiss and G. Schneider, Med. Chem. Commun., 2018, 9, 1538 DOI: 10.1039/C8MD00370J

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Spotlight

Advertisements