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

International audienceThe number of local minima of the potential energy landscape (PEL) of molecular systems generallygrows exponentially with the number of degrees of freedom, so that a crucial property of PEL explorationalgorithms 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 fosterthe exploration of yet unexplored regions. This ability is obtained by interleaving calls to the extensionprocedures of BH and T-RRT, and we show tuning the balance between these two types of calls allows thealgorithm to focus on low lying regions. Computational efficiency is obtained using state-of-the art datastructures, in particular for searching approximate nearest neighbors in metric spaces.We present results for the BLN69, a protein model whose conformational space has dimension 207and whose PEL has been studied exhaustively. On this system, we show that the propensity of ouralgorithm to explore low lying regions of the landscape significantly outperforms those of BH and T-RRT