The advent of recombinant protein-based therapeutic agents in the 1980s and subsequent waves of innovation in molecular biology and engineering of biologics has permitted the production of an increasingly broad array of complex, high molecular weight constructs. While this has opened a powerful new toolbox of molecular scaffolds with which to probe and interdict biological processes, it also makes deciphering the architectural nuances between individual constructs intuitively difficult. Key to downstream data processes for the detection of data trends is the ability to unambiguously identify, compare, and communicate the nature of molecular compositions. Existing small molecule orientated software tools are not intended for structures such as peptides, proteins, antibodies, and RNA, and do not contain adequate atomistic or domain-level detail to appropriately convey their higher structural complexity. Similarly, there is a paucity of large molecule-focused data analysis and visualization tools. This article will describe four new approaches we developed for the graphical representation and analysis of complex large molecules and experimental data. These tools help fulfill key needs in scientific communication and structure-property analysis of complex biologics and modified oligonucleotide-based drug candidates.

中文翻译：

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开发用于复杂生物制品和 RNA 模态及相关实验数据可视化分析的人机语言界面

20 世纪 80 年代基于重组蛋白的治疗剂的出现以及随后的分子生物学和生物制品工程创新浪潮使得越来越多的复杂、高分子量结构的生产成为可能。虽然这打开了一个强大的新分子支架工具箱，可以用来探测和阻断生物过程，但它也使得直观地破译各个结构之间的结构细微差别变得困难。用于检测数据趋势的下游数据处理的关键是能够明确识别、比较和传达分子组成的性质。现有的面向小分子的软件工具不适用于肽、蛋白质、抗体和 RNA 等结构，并且不包含足够的原子或领域级细节来适当地传达它们更高的结构复杂性。同样，也缺乏以大分子为中心的数据分析和可视化工具。本文将介绍我们为复杂大分子和实验数据的图形表示和分析开发的四种新方法。这些工具有助于满足复杂生物制品和基于修饰寡核苷酸的候选药物的科学交流和结构特性分析的关键需求。