剑桥结构数据库和结构动力学

Hans-Beat Bürgi
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引用次数: 0

摘要

随着剑桥结构数据库(CSD)中计算机可读信息的出现,对片段、分子和晶体结构进行广泛的、基本自动化的比较成为可能。他们的研究表明,在不同环境中出现的特定结构片段的原子间距离、角度和扭转角的分布彼此高度相关,而且与光谱信号、反应和活化能等其他观测指标也高度相关。这种相关性通常在很大的参数值范围内持续存在。它们让人联想到断键和成键反应、多面体重排和构象变化。它们定性地映射了分子动力学发生的结构参数空间区域,即各自(自由)能面的低能量区域。相关性的扩展性和连续性为大量结构数据提供了组织原则,并建议重新考虑以路易斯酸与路易斯碱相互作用为基础的键、"非键 "和 "非共价 "相互作用的传统定义和描述。我们选取了一些具有重要历史意义的例子以及后来的一些发展来说明这些方面。看来,CSD(和其他结构数据库)中的信息量以及关于这些信息的性质和关联性的知识,在不久的将来应该可以让人们利用机器学习方法对结构及其性质进行可信的推断和预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Cambridge Structural Database and structural dynamics
With the availability of the computer readable information in the Cambridge Structural Database (CSD), wide ranging, largely automated comparisons of fragment, molecular, and crystal structures have become possible. They show that the distributions of interatomic distances, angles, and torsion angles for a given structural fragment occurring in different environments are highly correlated among themselves and with other observables such as spectroscopic signals, reaction and activation energies. The correlations often extend continuously over large ranges of parameter values. They are reminiscent of bond breaking and forming reactions, polyhedral rearrangements, and conformational changes. They map—qualitatively—the regions of the structural parameter space in which molecular dynamics take place, namely, the low energy regions of the respective (free) energy surfaces. The extension and continuous nature of the correlations provides an organizing principle of large groups of structural data and suggests a reconsideration of traditional definitions and descriptions of bonds, “nonbonded” and “noncovalent” interactions in terms of Lewis acids interacting with Lewis bases. These aspects are illustrated with selected examples of historic importance and with some later developments. It seems that the amount of information in the CSD (and other structural databases) and the knowledge on the nature of, and the correlations within, this body of information should allow one—in the near future—to make credible interpolations and possibly predictions of structures and their properties with machine learning methods.
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