Computer assisted peptide design and optimization with topology preserving neural networks

Authors:
Jörg D. Wichard;Sebastian Bandholtz;Carsten Grötzinger;Ronald Kühne
Affiliations:
FMP Berlin, Berlin, Germany;Charité, Department of Hepatology and Gastroenterology, Berlin, Germany;Charité, Department of Hepatology and Gastroenterology, Berlin, Germany;FMP Berlin, Berlin, Germany
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Year:
2010

Citing 3
Cited 0

Neural networks and the bias/variance dilemma

Neural Computation
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.