## 4 Conclusions and Outlook  
In this study, we have presented a new global optimization algorithm, IGLOO, which combines the basic features of the RRT algorithm from the field of robotics, of the threshold algorithm that has in the past been used to study energy landscapes of various chemical and physical systems, and of repeated local stochastic quenches. Both the RRT and the threshold algorithm explore the landscape in many ways \"orthogonal\" to the standard global optimization and exploration procedures such as GA, SA, or BH. However, when combined with a moderately greedy local optimization algorithm, we find that IGLOO demonstrates a faster convergence to low-energy minima at a lower computational cost than the other two algorithms we have tested as a comparison (BH and HYBRID), even though they share tools such as frequent minimizations (BH and HYBRID) and the exploration capabilities of the RRT algorithm (HYBRID). In particular, IGLOO achieves a much smoother coverage of all important low-energy regions of the landscape and exhibits a high efficiency in exploring these zones on the landscape. Considering the tools common with the other algorithms, it appears that the threshold criterion likely makes a difference as it tends to focus the search  
Figure 4: Distribution of minimized conformations of df-c-Myb on a two-dimensional projection at three different CPU times (in s) for BH (red), HYBRID (blue), and IGLOO (green). The two dimensions are defined by the \({}^{4}C\alpha-^{7}Ca\) and \({}^{2}C\alpha-^{5}C\alpha\) interatomic distances (in Å). HPN1, HPN2 and HLX regions are depicted by oblique hatches, horizontal hatches and grids, respectively. On each thumbnail, the dark dots correspond to the minima already located during the CPU time corresponding to the previous thumbnail. on the low-energy regions of the landscape, without overly constraining the subsequent search inside these regions. In particular, by reducing the importance of lower-level energy barriers but still allowing the RRT-part of the search algorithm to stay in large regions that lie below a (possibly characteristic) threshold energy of the system, the combination of RRT, threshold, and local minimization results in a relatively fast yet efficient and homogeneous coverage of the various low-energy regions of the landscape. In this \"proof-of-principle\" study, we have illustrated the ability of IGLOO to identify a representative set of low-energy conformations of flexible peptides. Nevertheless, we can envisage many other applications. A particularly interesting application would be the search for conformers of (small) organic molecules in the context of virtual screening for drug discovery, where IGLOO could be competitive with respect to other algorithms [24] thanks to its ability to rapidly find diverse low-energy conformations. IGLOO could also be applied to the generation of accessible conformations from a given one with minor modifications of the algorithm, incorporating additional features of the threshold algorithm. Indeed, the main aspect to be changed is the initial iteration of the algorithm, which should start from a given conformation instead of a set of randomly sampled states, and the energy threshold(s) controlling the accessibility should be initialized at the desired value(s). Going further, IGLOO could find applications in many other fields beyond molecular modeling, wherever global optimization methods are useful, including hyperparameter optimization in machine learning [45]. Note, however, that due to the RRT-based exploration, IGLOO requires the definition of a suitable distance metric in the search space, which is not straightforward for all types of problems. Concerning potential future applications of IGLOO to various types of chemical systems consisting of one or more molecules, either isolated or on a surface or within a medium, two issues are expected to arise: (i) the quality of the energy function, and (ii) the dimensionality and topology of the configuration space that needs to be explored.