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2018 | OriginalPaper | Chapter

Construction of Heuristic for Protein Structure Optimization Using Deep Reinforcement Learning

Authors : Rok Hribar, Jurij Šilc, Gregor Papa

Published in: Bioinspired Optimization Methods and Their Applications

Publisher: Springer International Publishing

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Abstract

Deep neural networks are constructed that are able to partially solve a protein structure optimization problem. The networks are trained using reinforcement learning approach so that free energy of predicted protein structure is minimized. Free energy of a protein structure is calculated using generalized three-dimensional AB off-lattice protein model. This methodology can be applied to other classes of optimization problems and represents a step toward automatic heuristic construction using deep neural networks. Trained networks can be used to construct better initial populations for optimization. It is shown that differential evolution applied to protein structure optimization problem converges to better solutions when initial population is constructed in this way.

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Metadata
Title
Construction of Heuristic for Protein Structure Optimization Using Deep Reinforcement Learning
Authors
Rok Hribar
Jurij Šilc
Gregor Papa
Copyright Year
2018
Book
Bioinspired Optimization Methods and Their Applications

Print ISBN: 978-3-319-91640-8

Electronic ISBN: 978-3-319-91641-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-91641-5_13

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