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.

项目总结/摘要 两项新的虚拟化学技术将作为单独的模块加入NCATS ASPIRE项目。的 第一个模块将使新的化学品能够建模，并从尖端（深）机中选择 使用直接从仪器获取的最新结构/活动数据学习技术。第二模块 将是一个新的信息系统，用于捕获语义模板中的化学丰富的数据， 机器可读的反应，这将增加电子实验室笔记本中化学反应的实用性， 允许反应分析（及其相应的反应）的更精确的询问和自动化 产品）。 模块1中的深度学习技术基于我们新的化学丰富向量（CRV）方法， 它能够高效地将有关化学结构的信息压缩到64个数字的向量中 这使得编码过程可以逆转：不仅可以将CRV转换回其原始状态， 结构具有高成功率（>90%精确匹配），但修改的CRV可以转换为 在化学空间中的代表点。CRV是SAR/QSAR迭代的良好描述符 因为它们在一个小空间里包含了更多的化学信息， 结构-活性模型相对于常规描述符更加精简。由此产生的模型将 通过交互式视觉界面（人工指导）或后端探索多维空间 算法不断搜索新的和更好的结构（机器指导）。交互和 自动化流程将重新连接到ASPIRE自动化周期中， 合成和测量（假设评估和迭代优化）。 第二个模块是机器可读的反应，它借鉴了我们开发 BioHarmony Annotator（前身为BioAssay Express），使用自然语言模型分配语义 将本体术语转换为生物测定协议，将它们从非结构化文本转换为机器可读数据。 从协议和化学结构图中提取反应的全部内容非常困难 考虑到图表中的文本、缩写、快捷方式和假设的非结构化性质。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、反应后处理和产物表征）。作为 或者，我们将模块化的CDD化学计量素描，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 template.