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The effect of numerical error on the reproducibility of molecular geometry optimizations

机译:数值误差对分子几何优化的可重复性的影响

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摘要

Geometry optimization is one of the most often applied techniques in computational drug discovery. Although geometry optimization routines are generally deterministic, the minimization trajectories can be extremely sensitive to initial conditions, especially in case of larger systems such as proteins. Simple manipulations such as coordinate transformations (translations and rotations), file saving and retrieving, and hydrogen addition can introduce small variations (similar to 0.001 angstrom) in the starting coordinates which can drastically affect the minimization trajectory. With large systems, optimized geometry differences of up to 1 angstrom RMSD and final energy differences of several kcal/mol can be observed when using many commercially available software packages. Differences in computer platforms can also lead to differences in minimization trajectories. Here we demonstrate how routine structure manipulations can introduce small variations in atomic coordinates, which upon geometry optimization, can give rise to unexpectedly large differences in optimized geometries and final energies. We also show how the same minimizations run on different computer platforms can also lead to different results. The implications of these findings on routine computational chemistry procedures are discussed.
机译:几何优化是计算药物发现中最常用的技术之一。尽管几何优化例程通常是确定性的,但是最小化轨迹可能对初始条件极为敏感,尤其是在较大的系统(例如蛋白质)的情况下。简单的操作(例如坐标转换(平移和旋转),文件保存和检索以及添加氢)会在起始坐标中引入小的变化(类似于0.001埃),这会极大地影响最小化轨迹。对于大型系统,使用许多可商购的软件包时,可以观察到最高达1埃RMSD的最佳几何形状差异和几千卡/摩尔的最终能量差异。计算机平台的差异也会导致最小化轨迹的差异。在这里,我们演示了常规的结构操纵如何在原子坐标中引入小的变化,这在几何优化时会导致优化的几何形状和最终能量出现意想不到的巨大差异。我们还展示了在不同的计算机平台上运行相同的最小化如何也会导致不同的结果。讨论了这些发现对常规计算化学程序的影响。

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