Virtual Approaches to New Chemistries

新化学的虚拟方法

• 批准号: 10636882
• 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/10636882
• 关键词: Abbreviations; Algorithms; Automation; Back; Biological; Biological Assay; Categories; Characteristics; Chemical Structure; Chemicals; Chemistry; Collection; Data; Databases; Descriptor; Dimensions; Drug Design; Electronics; 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; Structure; System; Technology; Text; Update; Vendor; Visual; Work; chemical reaction; deep learning; design; drug development; experience; instrument; interactive tool; knowledge base; model building; 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.

项目概要/摘要 两项新的虚拟化学技术将作为单独的模块添加到 NCATS ASPIRE 项目中。这 第一个模块将使新化学物质能够从尖端（深）机器中建模和选择 使用直接从仪器获取的最新结构/活动数据来学习技术。第二模块 将是一种新颖的信息学系统，用于在语义模板中捕获丰富的化学数据，如下所示 机器可读的反应将增加电子实验室笔记本中化学反应的实用性 允许更精确的询问和反应分析的自动化（及其相应的反应 产品）。 模块 1 中的深度学习技术基于我们新的化学富载体 (CRV) 方法， 它能够高效地将化学结构信息压缩为 64 个数字的向量 允许逆转编码过程：CRV 不仅可以转换回其原始状态 结构具有很高的成功率（>90% 精确匹配），但修改后的 CRV 可以转换为以下结构： 代表化学空间中的那个点。 CRV 为 SAR/QSAR 迭代提供了出色的描述符 因为它们在很小的空间中包含更多的化学信息，从而可以实现自动化 相对于传统的描述符，结构-活动模型更加简化。由此产生的模型将 通过交互式视觉界面（人为导向）或后端探索多维空间 算法不断寻找新的和更好的结构（机器导向）。既可以互动又可以 自动化流程将重新连接到 ASPIRE 自动化循环中，以便它们可以 综合和测量（假设评估和迭代优化）。 第二个模块，机器可读的反应，借鉴了我​​们开发 BioHarmony Annotator（以前：BioAssay Express）使用自然语言模型来分配语义 将本体术语转化为生物检测协议，将其从非结构化文本转变为机器可读的数据。 从方案和化学结构图中提取反应的全部内容非常困难 考虑到图表中文本、缩写、快捷方式和假设的非结构化性质。这是 由于需要将方案中的材料与反应文本描述（例如， 试剂、溶剂、配方中涉及的序列、反应后处理和产品表征）。作为一个 或者，我们将对 CDD 化学计量草图进行模块化，这将使我们能够提取这些数据。我们将 与 NCATS 合作确定要捕获的重要字段，创建机器可读的化学反应 模板。