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Issue 9, 2018
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Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning

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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

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Publication details

The article was received on 23 Jul 2018, accepted on 07 Aug 2018 and first published on 22 Aug 2018


Article type: Research Article
DOI: 10.1039/C8MD00370J
Citation: Med. Chem. Commun., 2018,9, 1538-1546
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    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

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