Virtual Approaches to New Chemistries

新化学的虚拟方法

• 批准号: 10447249
• 负责人: BARRY A BUNIN
• 金额: $ 44万
• 依托单位: COLLABORATIVE DRUG DISCOVERY, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-06 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447249
• 关键词: Abbreviations; Address; Algorithms; Automation; Back; Biological; Biological Assay; Categories; Characteristics; Chemical Structure; Chemicals; Chemistry; Collection; Data; Databases; Descriptor; Drug Design; Evaluation; FAIR principles; Generations; Goals; Human; Informatics; Internet; Learning Module; Machine Learning; Measures; Methodology; Modeling; National Center for Advancing Translational Sciences; Natural Language Processing; Natural regeneration; Nature; Ontology; Process; Program Development; Protocols documentation; Quantitative Structure-Activity Relationship; Reaction; Readability; Reagent; Recipe; Research Personnel; Running; Scheme; Scientist; Semantics; Solvents; Sorting - Cell Movement; Structure; System; Technology; Text; Update; Vendor; Visual; Work; base; chemical reaction; deep learning; design; drug development; experience; instrument; interactive tool; knowledge base; natural language; new technology; novel; preference; small molecule; stoichiometry; success; tool; vector; virtual

Project Summary/Abstract Two new virtual chemistry technologies will be added to the NCATS ASPIRE project as separate modules. The first module will enable new chemistries to be modelled and selected from cutting edge (deep) machine learning technology using the latest structure/activity data taken directly from instruments. The second module will be a novel informatics system for capturing chemistry-rich data in a semantic template as machine-readable reactions which will increase the utility of chemical reactions in electronic lab notebooks and allow more precise interrogation and automation of reaction analyses (and their corresponding reaction products). The deep learning technology in module 1 is based on our new chemically rich vector (CRV) methodology, which is able to compress information about chemical structures into a vector of 64 numbers with an efficiency that allows the encoding process to be reversed: not only can a CRV be converted back into its original structure with high success (>90% exact match), but a modified CRV can be converted into a structure that is representative of that point in chemical space. CRVs make excellent descriptors for SAR/QSAR iteration because they contain much more chemical information in a small space, allowing the automation of structure-activity models to be more streamlined, relative to conventional descriptors. The resulting models will explore the multi-dimensional space via an interactive visual interface (human-directed) or a back-end algorithm to constantly search for new and better structures (machine-directed). Both interactive and automated processes will be connected back into the ASPIRE automation cycle so that they can be synthesized and measured (hypothesis evaluation and iterative optimization). The second module, machine-readable reactions, draws from our extensive experience developing the BioHarmony Annotator (formerly: BioAssay Express) which uses natural language models to assign semantic ontology terms to biological assay protocols, turning them from unstructured text into machine-readable data. Extracting the full content of reactions from protocols and chemical structure diagrams is remarkably difficult given the unstructured nature of text, abbreviations, shortcuts and assumptions that go into diagrams. It is further complicated by the need to connect the materials in the scheme with the reaction text description (e.g. reagents, solvents, the sequences involved in the recipe, reaction workup, and product characterization). As an alternative, we will modularize the CDD stoichiometric sketcher, which will allow us to extract this data. We will work with NCATS to identify important fields to capture, creating a machine readable chemical reaction template.

项目摘要/摘要 NCATS ASPIRE项目将以单独的模块添加两种新的虚拟化学技术。这 第一个模块将使新的化学物质能够建模并从尖端（深）机器中选择 使用直接从工具中获取的最新结构/活动数据的学习技术。第二个模块 将是一种新颖的信息系统，用于在语义模板中捕获化学丰富的数据作为 机器可读反应将增加电子实验室笔记本中化学反应的效用 允许对反应分析的更精确的询问和自动化（及其相应的反应 产品）。 模块1中的深度学习技术基于我们的新化学丰富向量（CRV）方法论， 能够将有关化学结构的信息压缩到具有效率的64个数字的向量中 这允许编码过程被颠倒：不仅可以将CRV转换为原始 成功的结构很高（> 90％精确匹配），但是修改后的CRV可以转换为一个结构 化学空间中该点的代表。 CRV为SAR/QSAR迭代提供了出色的描述符 因为它们在较小的空间中包含更多的化学信息，因此可以自动化 相对于常规描述符，结构活性模型更加精简。由此产生的模型将 通过交互式视觉界面（人为指导）或后端探索多维空间 算法不断搜索新的和更好的结构（机器定向）。互动和 自动化过程将被连接到Aspire自动化周期，以便它们可以 合成和测量（假设评估和迭代优化）。 第二个模块，机器可读的反应，从我们的丰富经验中汲取了发展 Bioharmony注释器（以前：Bioassay Express）使用自然语言模型来分配语义 生物测定协议的本体术语，将其从非结构化的文本转变为可读的数据。 从方案和化学结构图中提取反应的全部内容非常困难 鉴于文本的非结构化性质，缩写，快捷方式和图表的假设。这是 需要将方案中的材料与反应文本描述联系起来更加复杂（例如 试剂，溶剂，配方中涉及的序列，反应工作和产品表征）。作为 替代方案，我们将模块化CDD化学计量学素描器，这将使我们能够提取此数据。我们将 与NCAT合作以识别重要领域以捕获，创建机器可读的化学反应 模板。