Molecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations.

分子模拟是补充和解释生物分子模棱两可的实验数据以获得结构模型的有力工具。这种数据辅助模拟通常依赖于参数，参数的选择非常重要且对性能至关重要。关键挑战是相对于基础物理模型对实验信息进行加权。我们引入了 FLAPS，一种动态粒子群优化的自适应变体，以克服这个参数选择问题。FLAPS 适用于复合目标函数的优化，该目标函数取决于优化参数和附加的先验未知加权参数，这些参数对搜索空间拓扑结构有很大影响。这些加权参数是在运行时学习的，产生一个动态演化和迭代细化的搜索空间拓扑。作为一个实际示例，我们展示了如何使用 FLAPS 来查找小角度 X 射线散射引导的蛋白质模拟的功能参数。