Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning

Jens Alexander Fuchs, Francesca Grisoni, Michael Kossenjans, Jan A. Hiss, Gisbert Schneider

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1538-1546
Number of pages9
Issue number9
Publication statusPublished - 1 Sep 2018
Externally publishedYes

Bibliographical note

Funding Information:
We thank Sarah Haller, Christian Steuer and Ruth Alder for technical assistance. This research was financially supported by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (grant no. CR3212_159737).

Publisher Copyright:
© The Royal Society of Chemistry.


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