Discovery & Synthesis Chemputer: An intelligent universal system for automated chemical synthesis and discovery across different hardware and scales

发现

• 批准号: 10905022
• 负责人: GAURAV CHOPRA
• 金额: $ 21.72万
• 依托单位: UNIVERSITY OF GLASGOW
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10905022
• 关键词: Active Learning; Artificial Intelligence; Benchmarking; Chemicals; Chemistry; Code; Collaborations; Communities; Custom; Data Analyses; Data Set; Databases; Funding; Generations; Grant; Infrastructure; Instruction; Intelligence; Laboratories; Language; Life Cycle Stages; Literature; Modeling; Molecular Structure; National Center for Advancing Translational Sciences; Natural Language Processing; Procedures; Programming Languages; Reaction; Reporting; Research; Software Tools; Specific qualifier value; System; Testing; Training; Universities; Work; Writing; chemical synthesis; design; digital; experimental study; fitness; heuristics; open source; structured data; tool

Project Summary In this supplement to the collaborative project initiated between the Digital Chemistry Group at the University of Glasgow and The NCATS ASPIRE laboratory we will deepen the integration of the χDL chemical programming language with the Open Reaction Database (ORD) as well as integrating Large Language Model (LLM)-based AI approaches into the generation of χDL procedures directly from retrosynthetic analyses of target compounds. This work will be accomplished during the term of the original grant. Two specific aims are proposed: 1. Develop a χDL to ORD bridge which can be instantiated on a chemputer-based physical synthesis platform. (Coley Lab collaboration); 2. Integrate large language models (LLMs) within the Chemical Description Language (χDL) framework to generate develop and interface χDLs for closed-loop active learning infrastructures (Chopra Lab collaboration). These aims will be developed over the term of the funding in a highly integrated and collaborative working modus operandi. For specific aim one we will develop a set of converters bridging the three stages of the experimental life cycle: planning, execution, and reporting. This is achieved by integrating the planning and reporting stages, which can be fully represented by the structured data schema of the ORD, with the central stage of execution, which is fully expressible in χDL. These converters will include some level of inference, through heuristics or otherwise, to fill in procedural details that might not be explicitly defined in the original plan. They can also validate if a plan can be executed in a particular lab in terms of hardware compatibility. We will realize such converters as open-source software tools and test these tools on a chemputer hardware platform for a set of benchmark reactions. For specific aim two we will develop an extension to our Natural Language Processing (NLP) approach to χDL procedure generation by using generated data sets to train a LLM AI system to be able to produce χDL instruction files directly from retrosynthetic analysis of a desired molecular structure. This will be accomplished by building a custom set of LLM agents designed to utilize the χDL NLP model to interpret and write valid χDL code based on user input. By integrating these with the χDL blueprints which are being developed for benchmark reactions as part of the NCATS ASPIRE collaboration, these χDL instructions can then be generated from automated retrosynthetic analysis of a given molecule, or class of molecules even if the suggested reactions do not yet exist in the chemical literature. We will produce specifications for a further LLM based AI system to interpret the data generated by automatic analysis and to suggest new subsequent experiments based on a pre-defined fitness function optimization (for example yield or purity of products) which can be defined experimentally in the automated system.

项目摘要 在这项补充的补充中，在大学数字化学小组之间启动 格拉斯哥和NCATS ASPIRE实验室，我们将加深χDL化学编程的整合 具有开放反应数据库（ORD）的语言以及基于大型语言模型（LLM）的语言 AI直接从靶化合物的循环分析中直接进入χDL程序的生成。 这项工作将在原始赠款期间完成。提出了两个具体目标：1。发展 可以在基于化学的物理合成平台上实例化的ORD桥的χDL。 （科利实验室 合作）; 2。将大型语言模型（LLM）整合到化学描述语言（χDL）中 用于生成开发和界面χDL的框架，用于闭环主动学习基础设施（Chopra Lab 合作）。这些目标将在高度融合和协作的资金期间开发 工作作案手法。对于特定目标，我们将开发一组桥接的转换器 实验生命周期：计划，执行和报告。这是通过整合计划和 报告阶段，可以由ORD的结构化数据模式完全表示，并带有中央 执行阶段，在χDL中完全表达。这些转换器将包括一定程度的推理， 通过启发式方法或其他方式填写原始计划中可能未明确定义的程序细节。 他们还可以验证是否可以根据硬件兼容性在特定实验室执行计划。我们将 实现开源软件工具等转换器，并在Chemputer硬件平台上测试这些工具 用于一组基准反应。对于特定目标两个，我们将开发自然语言的扩展 通过使用生成的数据集训练LLM AI系统，处理（NLP）方法生成χDL过程 能够直接通过对所需分子结构的递归合成分析产生χDL指令文件。 这将通过构建旨在利用χDLNLP模型的自定义LLM代理来实现 根据用户输入来解释和写入有效的χDL代码。通过将它们与χDL蓝图集成在一起 作为NCATS ASPIRE协作的一部分，是为基准反应开发的，这些χDL说明 然后，可以通过对给定分子的自动反折分析或分子类别产生 如果建议的反应在化学文献中尚不存在。我们将产生进一步的规格 基于LLM的AI系统来解释通过自动分析生成的数据，并建议新的后续序列 基于预定义的健身功能优化（例如产量或产品的纯度）的实验，该功能 可以在自动化系统中实验定义。