Computer Assisted Peptide Design and Optimization with Topology Preserving Neural Networks
We propose a non-standard neural network called TPNN which offers the direct mapping from a peptide sequence to a property of interest in order to model the quantitative structure activity relation. The peptide sequence serves as a template for the network topology. The building blocks of the network are single cells which correspond one-to-one to the amino acids of the peptide. The network training is based on gradient descent techniques, which rely on the efficient calculation of the gradient by back-propagation. The TPNN together with a GA-based exploration of the combinatorial peptide space is a new method for peptide design and optimization. We demonstrate the feasibility of this method in the drug discovery process.
KeywordsTraining Sample Training Error Cellular Neural Network Quantitative Structure Activity Relation Metabolic Stability
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