We report in the present work a new method for exploring conformational energy landscapes.
The method, called T-RRT, combines ideas from statistical physics and robot path planning
algorithms. A search tree is constructed on the conformational space starting from a given state.
The tree expansion is driven by a double strategy: on the one hand, it is naturally biased towards
yet unexplored regions of the space; on the other, a Monte Carlo-like transition test guides the
expansion toward energetically favorable regions. The balance between these two strategies is
automatically achieved thanks to a self-tuning mechanism. The method is able to efficiently find
both, energy minima and transition paths between them. As a proof of concept, the method is
applied to two academic benchmarks and to the alanine dipeptide.
CitacióJaillet, L. [et al.]. Randomized tree construction algorithm to explore energy landscapes. "Journal of computational chemistry", Desembre 2011, vol. 32, núm. 16, p. 3464-3474.