Informatics and Machine Learning Modules for Research Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous Laboratory

用于 ASPIRE 自主实验室研究规划、调度、模拟和优化的信息学和机器学习模块

• 批准号: 10448106
• 负责人: Connor Wilson Coley
• 金额: $ 56.25万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-10 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10448106
• 关键词: Address; Adoption; Algorithms; Artificial Intelligence; Automation; Benchmarking; Biological Assay; Biological Testing; Chemicals; Chemistry; Collection; Communication; Communities; Complex; Computer Assisted; Computer software; Consumption; Data; Data Science; Decision Making; Development; Disease; Documentation; Evaluation; Family; Feedback; Goals; Graph; Health; Human; Informatics; Infrastructure; Laboratories; Learning; Learning Module; Literature; Machine Learning; Manuals; Medicine; Modeling; Molecular; National Center for Advancing Translational Sciences; Performance; Pharmaceutical Chemistry; Pharmacologic Substance; Procedures; Process; Property; Publishing; Reaction; Readability; Recommendation; Research; Route; Running; Schedule; Structure; Synthesis Chemistry; Therapeutic; Time; Translations; Twin Multiple Birth; Visualization; Visualization software; Work; application programming interface; base; chemical reaction; chemical synthesis; cheminformatics; cost; data-driven model; deep learning; design; digital; drug candidate; drug discovery; experimental study; graphical user interface; improved; innovation; interest; knowledge graph; lead optimization; negative affect; novel; novel therapeutics; open source; open source tool; operation; preclinical development; predictive modeling; process optimization; programs; response; screening; simulation; small molecule; software development; theories; therapeutic candidate; tool; virtual

PROJECT SUMMARY Access to complex chemical matter (e.g., small molecule drug candidates) is a core requirement for testing biological hypotheses and probing human health. Current approaches to chemical synthesis rely on time-consuming planning and labor-intensive manual synthesis, which is a rate-limiting step in the discovery of new functional molecules. This collaborative project comprises the development of several virtual modules to support the multi-step chemical synthesis of new molecules in autonomous laboratories. These modules are designed to benefit traditional synthetic chemists in addition to automation chemists using the integrated hardware platform being developed by the ASPIRE team at NCATS. Computer-aided synthesis planning can be viewed as a hierarchical process of elaboration starting from the list of molecules of interest: (1) retrosynthetic planning to identify suitable starting materials and intermediates, (2) reaction condition recommendation to identify the conditions with which each reaction step should be run, (3) translation of hypothetical reaction steps into action sequences executable on automated hardware. Optional but valuable components include (4) recording procedures through an experimental planning module, (5) optimization of the timing and order of action sequences to most efficiently synthesize multiple synthetic targets via a digital twin of the platform, and (6) the iterative optimization of process parameters based on experimental responses in a feedback loop. This program will address each of these needs through the development of new software solutions employing state of the art algorithms in graph network theory, cheminformatics, deep learning for chemistry, and optimization. Software modules will be written using established software development best practices for ease of cross-platform deployment (via containerization) and long-term maintainability (via extensive documentation). Further, each module will be deployed as an independent microservice with a common application programming interface (API) format for inter-module communication and integration with existing NCATS modules, including graphical user interfaces. These efforts will be accomplished through close partnership between MIT and NCATS to enhance the overall capabilities of the NCATS ASPIRE platform.

项目摘要 获取复杂的化学物质（例如，小分子候选药物）是核心要求 用于测试生物学假设和探测人类健康。当前的化学方法 合成依赖于耗时的计划和劳动密集型手动合成，这是一个 发现新功能分子的速率限制步骤。这个协作项目 包括几个虚拟模块的开发以支持多步化学 自治实验室中新分子的合成。这些模块设计为 除了使用集成的自动化化学家外，受益于传统的合成化学家 硬件平台由NCAT的Aspire团队开发。计算机辅助合成 可以将计划视为从列表开始 感兴趣的分子：（1）循环合成计划，以识别合适的起始材料和 中间体，（2）反应条件建议，以确定每个条件 应进行反应步骤，（3）将假设反应步骤转换为动作序列 可在自动硬件上执行。可选但有价值的组件包括（4）录制 通过实验规划模块的程序，（5）优化时间和顺序 最有效地通过数字双胞胎合成多个合成目标的动作序列 平台和（6）基于实验的过程参数的迭代优化 反馈循环中的响应。该程序将通过 开发采用图形网络中采用艺术算法的新软件解决方案的开发 理论，化学信息学，化学深度学习和优化。软件模块将 使用既定的软件开发最佳实践书写，以便于跨平台 部署（通过容器化）和长期可维护性（通过广泛 文档）。此外，每个模块将作为独立的微服务部署 用于模块间通信的常见应用程序编程接口（API）格式 与现有的NCAT模块集成，包括图形用户界面。这些努力会 通过MIT和NCAT之间的密切合作伙伴关系，以增强整体 NCATS ASPIRE平台的功能。