Hybridizing rapidly growing random trees and basin hopping yields an improved exploration of energy landscapes
L'hybridation d'arbres aléatoires d'exploration et de basin hopping conduit à une exploration plus efficace des paysages énergétiques
Résumé
The number of local minima of the 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 growing 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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