Virtual Approaches to New Chemistries

新化学的虚拟方法

基本信息

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
项目类别：
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

项目摘要

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。