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Finding Optimal Discretization Orders for Molecular Distance Geometry by Answer Set Programming

机译:通过答案集编程找到分子距离几何的最佳离散化阶

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The Molecular Distance Geometry Problem (MDGP) is the problem of finding the possible conformations of a molecule by exploiting available information about distances between some atom pairs. When particular assumptions are satisfied, the MDGP can be discretized, so that the search domain of the problem becomes a tree. This tree can be explored by using an interval Branch & Prune (7BP) algorithm. In this context, the order given to the atoms of the molecules plays an important role. In fact, the discretization assumptions are strongly dependent on the atomic ordering, which can also impact the computational cost of the iBP algorithm. In this work, we propose a new partial discretization order for protein backbones. This new atomic order optimizes a set of objectives, that aim at improving the iBP performances. The optimization of the objectives is performed by Answer Set Programming (ASP), a declarative programming language that allows to express our problem by a set of logical constraints. The comparison with previously proposed orders for protein backbones shows that this new discretization order makes iBP perform more efficiently.
机译:分子距离几何问题(MDGP)是通过利用有关某些原子对之间距离的可用信息来发现分子的可能构象的问题。当满足特定假设时,可以将MDGP离散化,以便问题的搜索范围变成一棵树。可以使用间隔分支与修剪(7BP)算法探索该树。在这种情况下,赋予分子原子的顺序起着重要的作用。实际上,离散化假设很大程度上取决于原子顺序,这也可能会影响iBP算法的计算成本。在这项工作中,我们提出了蛋白质骨架的新的部分离散化顺序。这个新的原子顺序优化了一组旨在改善iBP性能的目标。目标的优化由答案集编程(ASP)执行,这是一种声明性编程语言,可以通过一组逻辑约束来表达我们的问题。与先前提出的蛋白质主干顺序的比较表明,这种新的离散顺序使iBP的执行效率更高。

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