Topology preserving neural networks for peptide design in drug discovery

Abstract

We describe a construction method and a training procedure for a topology preserving neural network (TPNN) in order to model the sequence-activity relation of peptides. The building blocks of a TPNN are single cells (neurons) which correspond one-to-one to the amino acids of the peptide. The cells have adaptive internal weights and the local interactions between cells govern the dynamics of the system and mimic the topology of the peptide chain. The TPNN can be trained by gradient descent techniques, which rely on the efficient calculation of the gradient by back-propagation. We show an example how TPNNs could be used for peptide design and optimization in drug discovery. © 2009 Springer Berlin Heidelberg.