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.

Keywords: dimensionality reduction; machine learning; membrane; neural network; peptide design.