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

项目概要 获取复杂化学物质（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索人类健康。目前的化学方法 合成依赖于耗时的计划和劳动密集型的手动合成，这是一个 发现新功能分子的限速步骤。本次合作项目 包括开发多个虚拟模块以支持多步化学 在自主实验室中合成新分子。这些模块旨在 除了使用集成的自动化化学家之外，传统合成化学家也受益 硬件平台由 NCATS 的 ASPIRE 团队开发。计算机辅助合成 规划可以被视为从清单开始的分层阐述过程 感兴趣的分子：（1）逆合成计划以确定合适的起始材料和 中间体，（2）反应条件建议，以确定每个中间体的条件 应运行反应步骤，(3) 将假设的反应步骤转换为操作序列 在自动化硬件上可执行。可选但有价值的组件包括 (4) 录音 通过实验计划模块的程序，（5）时间和顺序的优化 的动作序列，通过数字孪生最有效地合成多个合成目标 (6)基于实验的工艺参数迭代优化 反馈循环中的响应。该计划将通过 在图网络中采用最先进的算法开发新的软件解决方案 理论、化学信息学、化学深度学习和优化。软件模块将 使用已建立的软件开发最佳实践进行编写，以便于跨平台 部署（通过容器化）和长期可维护性（通过广泛的 文档）。此外，每个模块将部署为独立的微服务 用于模块间通信的通用应用程序编程接口（API）格式 与现有 NCATS 模块集成，包括图形用户界面。这些努力将 通过麻省理工学院和 NCATS 之间的密切合作来实现，以提高整体 NCATS ASPIRE 平台的功能。

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.