Konrad-Zuse-Zentrum
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ZIB PaperWeb
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| 00-51 | Tobias Galliat, Peter Deuflhard, Rainer Roitzsch, Frank Cordes
Automatic Identification of Metastable Conformations via Self-Organized Neural Networks |   |
Abstract: As has been shown recently, the identification of metastable chemical
conformations leads to a Perron cluster eigenvalue problem
for a reversible
Markov operator. Naive discretization of this operator
would suffer from
combinatorial explosion. As a first remedy, a
pre-identification of
essential degrees of freedom out of the set of torsion
angles had been
applied up to now. The present paper suggests a different
approach based on
neural networks: its idea is to discretize the Markov
operator via
self-organizing (box) maps. The thus obtained box
discretization then
serves as a prerequisite for the subsequent Perron cluster
analysis.
Moreover, this approach also permits exploitation of
additional structure
within embedded simulations. As it turns out, the new
method is fully
automatic and efficient also in the treatment of
biomolecules. This is
exemplified by numerical results.
Keywords: biochemical conformations,
cluster analysis,
Molecular Dynamics,
Monte-Carlo methods,
operator discretization,
Perron cluster
MSC: 68T05, 60J20, 62H30, 92-08