Prediction of intramolecular polarization of aromatic amino acids using kriging machine learning

Timothy L. Fletcher, Stuart J. Davie, Paul L A Popelier

    Research output: Contribution to journalArticlepeer-review


    © 2014 American Chemical Society.Present computing power enables novel ways of modeling polarization. Here we show that the machine learning method kriging accurately captures the way the electron density of a topological atom responds to a change in the positions of the surrounding atoms. The success of this method is demonstrated on the four aromatic amino acids histidine, phenylalanine, tryptophan, and tyrosine. A new technique of varying training set sizes to vastly reduce training times while maintaining accuracy is described and applied to each amino acid. Each amino acid has its geometry distorted via normal modes of vibration over all local energy minima in the Ramachandran map. These geometries are then used to train the kriging models. Total electrostatic energies predicted by the kriging models for previously unseen geometries are compared to the true energies, yielding mean absolute errors of 2.9, 5.1, 4.2, and 2.8 kJ mol-1for histidine, phenylalanine, tryptophan, and tyrosine, respectively.
    Original languageEnglish
    Pages (from-to)3708-3719
    Number of pages11
    JournalJournal of Chemical Theory and Computation
    Issue number9
    Publication statusPublished - 9 Sept 2014


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