Hybridizing rapidly growing random trees and basin hopping yields an improved exploration of energy landscapes

Abstract : The number of local minima of the potential energy landscape (PEL) of molecular systems generally grows exponentially with the number of degrees of freedom, so that a crucial property of PEL exploration algorithms is their ability to identify local minima which are low lying and diverse. In this work, we present a new exploration algorithm, retaining the ability of basin hopping (BH) to identify local minima, and that of transition based rapidly exploring random trees (T-RRT) to foster the exploration of yet unexplored regions. This ability is obtained by interleaving calls to the extension procedures of BH and T-RRT, and we show tuning the balance between these two types of calls allows the algorithm to focus on low lying regions. Computational efficiency is obtained using state-of-the art data structures, in particular for searching approximate nearest neighbors in metric spaces. We present results for the BLN69, a protein model whose conformational space has dimension 207 and whose PEL has been studied exhaustively. On this system, we show that the propensity of our algorithm to explore low lying regions of the landscape significantly outperforms those of BH and T-RRT.
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Article dans une revue
Journal of Computational Chemistry, Wiley, 2016, 37 (8), pp.14
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Contributeur : Frederic Cazals <>
Soumis le : jeudi 29 décembre 2016 - 09:50:34
Dernière modification le : vendredi 12 janvier 2018 - 01:48:58

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  • HAL Id : hal-01423282, version 1

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Andrea Roth, Tom Dreyfus, Charles H Robert, Frédéric Cazals. Hybridizing rapidly growing random trees and basin hopping yields an improved exploration of energy landscapes. Journal of Computational Chemistry, Wiley, 2016, 37 (8), pp.14. 〈hal-01423282〉

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