FFLUX: Transferability of Polarizable Machine-learned Electrostatics in Peptide Chains

Research output: Research - peer-reviewArticle


The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a kriging model. Such a knowledgeable atom needs to be informed about a sufficiently large environment around it. The resulting complexity can be tackled by collecting the 20 natural amino acids into a few groups. Using substituted deca-alanines, we present the proof-of-concept that a given atom's charge can be modeled by a few kriging models only.

Bibliographical metadata

Original languageEnglish
Pages (from-to)1005–1014
Number of pages10
JournalJournal of Computational Chemistry
Issue number13
Early online date10 Mar 2017
StatePublished - 15 May 2017