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Implementation of the Three-Dimensional-Pattern Search Problem on Hopfield-like Neural Networks

 

作者: E. Feuilleaubois,   V. Fabart,   J.P. Doucet,  

 

期刊: SAR and QSAR in Environmental Research  (Taylor Available online 1993)
卷期: Volume 1, issue 2-3  

页码: 97-114

 

ISSN:1062-936X

 

年代: 1993

 

DOI:10.1080/10629369308028822

 

出版商: Taylor & Francis Group

 

关键词: pharmacophore search;combinatorial optimization;simulated annealing;neural networks;Boltzmann machine

 

数据来源: Taylor

 

摘要:

The three-dimensional (3D)-pattern search problem can be summarized as finding, in a molecule, the subset of atoms that have the most similar spatial arrangement as those of a given 3D pattern. For this NP-complete combinatorial optimization problem we propose, by analogy to the travelling salesman problem, a new method taking advantage of the capability of Hopfield-like neural networks to carry out combinatorial optimization of an objective function. This objective function is built from the sum of the differences of interatomic distances in the pattern and the molecule. Here we present the implementation we have found of the 3D-pattern search problem on Hopfield-like neural networks. Initial tests indicate that this approach not only successfully retrieves a given pattern, but can also suggest partial solutions having one or two atoms less than the given pattern, an interesting feature in the case of local conformational flexibility of the molecule. The distributed representation of the problem on Hopfield-like neural networks offers a good perspective for parallel implementation.

 

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