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 D _7.4 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 hexapepti

SUBMITTER: Fuchs JA 

PROVIDER: S-EPMC6151477 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature