首页> 外文会议>Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2012 IEEE Symposium on >Molecular distance geometry optimization using geometric build-up and evolutionary techniques on GPU
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Molecular distance geometry optimization using geometric build-up and evolutionary techniques on GPU

机译:在GPU上使用几何构建和进化技术优化分子距离几何

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摘要

We present a combination of methods addressing the molecular distance problem, implemented on a graphic processing unit. First, we use geometric build-up and depth-first graph traversal. Next, we refine the solution by simulated annealing. For an exact but sparse distance matrix, the build-up method reconstructs the 3D structures with a root-mean-square error (RMSE) in the order of 0.1 Å. Small and medium structures (up to 10,000 atoms) are computed in less than 10 seconds. For the largest structures (up to 100,000 atoms), the build-up RMSE is 2.2 Å and execution time is about 540 seconds. The performance of our approach depends largely on the graph structure. The SA step improves accuracy of the solution to the expense of a computational overhead.
机译:我们提出了一种在图形处理单元上实现的解决分子距离问题的方法组合。首先,我们使用几何构造和深度优先图遍历。接下来,我们通过模拟退火优化解决方案。对于精确但稀疏的距离矩阵,构建方法可重建3D结构,其均方根误差(RMSE)约为0.1Å。不到10秒即可计算出中小型结构(最多10,000个原子)。对于最大的结构(最多100,000个原子),堆积的RMSE为2.2Å,执行时间约为540秒。我们方法的性能在很大程度上取决于图的结构。 SA步骤提高了解决方案的准确性,但以计算开销为代价。

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