COMPUTER-ASSISTED STRUCTURE ELUCIDATION

生物大分子在其化学结构上的表征 光谱特性在生物医学中具有广泛的重要性 科学。由于其复杂性，结构表征具有全局性 这是一项耗时的活动，基本上留给了 菲尔德。这项研究的目标是大幅增加 这些专家的工作效率。 鉴于这位专家精通于轻易挑选出正确的结构 在有限的可供选择的结构中，有许多是未知的 如果专家能够更快地分配结构， 可以用这样一个概念上的结构表征问题开始 看似合理的替代方案数量可控。因此，该计划的目标是 研究是创造基于人工智能的计算机软件 能够直接降低集体光谱性质(红外， 质子和碳-13核磁共振、紫外线和质量 光谱数据)到概念上可管理的数量 立体化学定义的结构，每一种都与 观测到的光谱数据。专家仍然是必备的玩家，但 省去了《手册》中实际上耗时的部分 生产有限数量的替代品的过程。 将创建的软件的主要功能包括：(1)两者都适用的程序 频谱解释和结构生成，(2)规定接受 专家已知的任何结构信息以及(3)能够 处理真实世界的结构表征问题，而不考虑 未知事物所属的结构类。最后命名的要素 需要不依赖于光谱库的系统。

The characterization of the structure of a biomolecule on the its chemical and spectral properties is of widespread importance in the biomedical sciences. Because of its complexity, structure characterization is overall a time-consuming activity that has been largely left to experts in the field. The goal of this research is to substantially increase the productivity of those experts. Given the expert's proficiency in readily picking out the correct structure of an unknown from among a limited number of alternative structures, a much more expeditious assignment of structure would be achieved if the expert could begin the structure characterization problem with such a conceptually manageable number of plausible alternatives. Thus, the goal of the research is to create artificial intelligence based computer software capable of directly reducing the collective spectral properties (infrared, proton and carbon-13 nuclear magnetic resonance, ultraviolet and mass spectral data) of the unknown to a conceptually manageable number of stereochemically defined structures, each of which is compatible with the observed spectral data. The expert is still a required player, but is spared what is actually the time-intensive component of the "manual" process, that of producing the limited number of alternatives. Key features of the software to be created include, (1) programs for both spectrum interpretation and structure generation, (2) provision to accept any structural information known to the expert and (3) the capability to treat real-world structure characterization problems without regard to the class of structures to which the unknown belongs. The last named feature requires a system that is not spectral library dependent.