title: Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides.
abstract: We present a "deep" network architecture for chemical data analysis and classification together with a prospective proof-of-concept application. The model features a self-organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two-dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence-length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.
authors.affiliation: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.|inSili.com LLC, Segantinisteig 3, CH-8049, Zurich, Switzerland.
authors.forename: Petra
authors.initials: P
authors.lastname: Schneider
authors.affiliation: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.
authors.forename: Alex T
authors.initials: AT
authors.lastname: Müller
authors.affiliation: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.
authors.forename: Gisela
authors.initials: G
authors.lastname: Gabernet
authors.affiliation: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.
authors.forename: Alexander L
authors.initials: AL
authors.lastname: Button
authors.affiliation: Paris-Lodron Universität Salzburg, Division of Microbiology, Billroth Str. 11, A-5020 Salzburg, Austria.
authors.forename: Gernot
authors.initials: G
authors.lastname: Posselt
authors.affiliation: Paris-Lodron Universität Salzburg, Division of Microbiology, Billroth Str. 11, A-5020 Salzburg, Austria.
authors.forename: Silja
authors.initials: S
authors.lastname: Wessler
authors.affiliation: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.
authors.forename: Jan A
authors.initials: JA
authors.lastname: Hiss
authors.affiliation: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.
authors.forename: Gisbert
authors.initials: G
authors.lastname: Schneider
mesh_terms: D023181:Antimicrobial Cationic Peptides / Q000737:chemistry* / Q000494:pharmacology; D000069550:Machine Learning; D025341:Principal Component Analysis; D013211:Staphylococcus aureus / Q000187:drug effects
chemical_list: D023181:Antimicrobial Cationic Peptides
keywords: dimensionality reduction; machine learning; membrane; neural network; peptide design