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A self‐assembling neural network for modeling polymers

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Abstract

A central problem in modeling protein and other polymer structures is the generation of self‐avoiding chains which obey a priori distance restraint information which could include a folding potential function. This problem is usually addressed with a lattice model or a torsion space model of the polymer. Exhaustive searches in these spaces are of necessity exponentially complex. A new computer algorithm for modeling polymers and polymeric systems is described. This algorithm is a randomized algorithm based on a self‐assembling or Kohonen neural network. Given a defined chain topology, a defined spatial extent, and a prior probability distribution, it finds a set of coordinates which reproduce these properties. The convergence rate of the algorithm is linear with respect to the number of distance terms included. Modifications to the standard Kohonen algorithm to include a defined spatial metric, and a modified update rule improve the convergence of the standard algorithm and result in a highly efficient algorithm for building polymer models which are self avoiding and consistent with prior probability information and interatomic distance restraints.

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Authors and Affiliations

  1. Department of Microbiology, Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA

    Robert W. Harrison

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  1. Robert W. Harrison
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Harrison, R.W. A self‐assembling neural network for modeling polymers. Journal of Mathematical Chemistry 26, 125–137 (1999). https://doi.org/10.1023/A:1019181811090

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