Development of A Specialized Platform for Innovative Research Exploration (ASPIRE)

开发创新研究探索专业平台（ASPIRE）

• 批准号: 10908200
• 负责人: Samuel Michael
• 金额: $ 635.56万
• 依托单位: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10908200
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The nature of the ASPIRE program is such that the NCATS intramural program will not build it alone; rather it will be built in conjunction with extramural researchers who are leaders in the field of chemical and biological automation, engineering, informatics, and infrastructure. There were five (5) awards made in total; two (2) for physical hardware modules, and three (3) for virtual hardware modules. These modules will be developed in collaboration with NCATS during the UG3 period of the grant, and if successful, will be incorporated into the overall ASPIRE platform during the UH3 phase. Two Hardware Modules 1) U Glasgow (Lee Cronin) BioChemputer: An Intelligent Universal System for Running Automated Chemical Reactions Across Different Hardware and Scales NCATS Chemputer built using shared blueprints and testing plans underway, 2) Purdue U (Graham Cooks) High Throughput Infrastructure for Reaction Screening and Biology Factory and site acceptance testing completed for NCATS-bound platform using novel mass spec-based technique to synthesize and perform bioassays on compounds at nanogram scale. Three Virtual Modules 1) MIT (Connor Coley) Informatics and Machine Learning Modules for Reaction Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous Laboratory Using NCATS benchmark chemical reactions for optimization, 2) Purdue/IBRI/OnAI (Gaurav Chopra) Chemical Instruments-aware Distributed Blockchain Based Open AI Platform to Accelerate Drug Discovery NCATS BioRAPTR 2.0 dispenser in use as 1st real-world integration with Purdue AI platform, 3) Collaborative Drug Discovery (Barry Bunin) Virtual Approaches to New Chemistries NCATS chemists/data scientists testing platform for analysis, and designing integrated digital representations of molecules to navigate bioactivity and chemical reactivity. In this collaborative project we will develop a system for the automation and execution of chemical reactions across a range of hardware and scales for the synthesis of known and unknown molecules. This work will leverage the last 6 years of progress in our laboratory on the Chemputer (a programmable chemical synthesis robot) and the principle of Chemputation (the concept that a chemical synthesis expressed in a type of chemical code can be run on any compatible hardware reliably). Three specific aims are proposed: 1. To develop the chemical programming language standard that will run the synthesis protocols; 2. Development of modular plug and play hardware for chemical synthesis including interfaces to third party systems; and 3. Creation of a reaction screening system for chemical synthesis and discovery. Mass spectrometry (MS) is a powerful and widely applicable analytical method for qualitative and quantitative analysis of compounds of all types and sizes. Desorption electrospray ionization (DESI) is an ambient ionization method in which samples are analyzed in the open air by impact of primary droplets. Given the ability to position an array of samples relative to the mass spectrometer, DESI-MS becomes a high throughput (HT) chemical analysis method. The power of MS as an analytical method is well known but it is less commonly realized that MS is also a preparative method, e.g. it can be used to deposit mass-selected ions on surfaces to create new materials. A significant application from the point of view of organic synthesis, is that MS can be used to create microdroplets from reaction mixtures and, therefore, many reactions in confined volumes (especially in microdroplets) can be accelerated relative to their rates in bulk, often by orders of magnitude. A unique feature of DESI is that the spray of solvent used to analyze a reaction mixture generates secondary droplets upon impact and the reagents can react in the solution phase while the droplets are carried to the mass spectrometer. It is this remarkable feature that makes DESI-MS a powerful synthetic method combined with a built-in analytical capability. 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. Chemical instruments-aware distributed blockchain based open AI platform to accelerate drug discovery Artificial Intelligence (AI) and Automation has the potential to accelerate several stages of the drug discovery process, including the design-make-test-analyze optimization cycle, typically faced by medicinal chemists. However, several roadblocks exist resulting in too long timelines to deliver much needed innovation to patients with unmet needs. Both human and AI face similar limitations mainly due to disjointed steps needed to obtain and integrate the data that is generated by different organizations or laboratories and cannot be readily shared without disclosing IP sensitive information (e.g., non-patented novel chemical structures). In addition, there is lack of negative (failures) data available publicly, which are critical for generating accurate AI models, but are typically not made available outside of the originating institution or laboratory due to a variety of reasons related to IP. And, even among positive results, greater reproducibility of protocols is desirable. A solution to develop a fully integrated system in-house can be effective but it is hard to scale and not easily adopted mainly due to the costs and infrastructure involved. Our solution encapsulates the vision of NCATS ASPIRE program of integrating and automating laboratories to accelerate the drug discovery process while taking into account the above problems that exist. Blockchain, a distributed ledger technology, coupled with AI and Automation has the potential to solve all of the above problems as it has done in several other technology sectors, such as finance and medicine to securely share and learn from data without revealing its identity. We will develop a blockchain based open science AI framework as a decentralized laboratory cloud for the drug discovery community to enhance collaboration and reproducibility. Proposed as a modular component that will fit in with the large-scale automated synthesis program at NCATS and interoperate with other informatics tools such as retrosynthetic analysis and inventory management. The central technological innovation is our method for ttraining a deep neural network to do a graph-to-graph transformation. In the middle of the network is a chemically rich vector () which has two important properties: (1) it captures the structural diversity of the input molecules using a short vector of highly orthogonal values, making it highly effective for QSAR models without needing the additional descriptor-pruning step; and (2) vectors can be mapped back to the molecular space to recreate the original structure.

Aspire计划的性质使得NCAT的壁内计划不会单独构建它。相反，它将与是化学和生物自动化，工程，信息学和基础设施领域的领导者一起建造的。 总共有五（5）个奖项；物理硬件模块的两个（2），虚拟硬件模块的三（3）个。 这些模块将在赠款的UG3期间与NCAT合作开发，如果成功的话，将在UH3阶段将其纳入整个Aspire平台。 Two Hardware Modules 1) U Glasgow (Lee Cronin) BioChemputer: An Intelligent Universal System for Running Automated Chemical Reactions Across Different Hardware and Scales NCATS Chemputer built using shared blueprints and testing plans underway, 2) Purdue U (Graham Cooks) High Throughput Infrastructure for Reaction Screening and Biology Factory and site acceptance testing completed for NCATS-bound platform using novel mass spec-based以纳米图尺度上的化合物合成和执行生物测定的技术。 Three Virtual Modules 1) MIT (Connor Coley) Informatics and Machine Learning Modules for Reaction Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous Laboratory Using NCATS benchmark chemical reactions for optimization, 2) Purdue/IBRI/OnAI (Gaurav Chopra) Chemical Instruments-aware Distributed Blockchain Based Open AI Platform to Accelerate Drug Discovery NCATS BioRAPTR 2.0 dispenser in用作与Purdue AI平台的第一现实整合，3）对新化学的合作药物发现（Barry Bunin）虚拟方法NCATS NCATS化学家/数据科学家测试分析的平台，并设计分子的综合数字表示，以导航生物活性和化学反应。 在这个协作项目中，我们将开发一个系统，用于在一系列硬件和尺度上自动化和执行化学反应，以综合已知和未知分子。这项工作将利用我们实验室在化学剂（可编程化学合成机器人）和化学原理（可以在任何兼容硬件上表达的化学合成概念）的概念来利用过去6年的进度（可编程化学合成机器人）。提出了三个具体目标：1。开发将运行合成协议的化学编程语言标准； 2。开发用于化学合成的模块化插头和播放硬件，包括与第三方系统的接口；和3。创建用于化学合成和发现的反应筛选系统。 质谱（MS）是一种强大的，广泛适用的分析方法，用于对各种类型和尺寸的化合物进行定性和定量分析。解吸电喷雾电离（DESI）是一种环境电离方法，其中通过原代液滴的影响在露天分析样品。鉴于能够相对于质谱仪定位样品阵列，DESI-MS成为高通量（HT）化学分析方法。 MS作为一种分析方法的力量是众所周知的，但很少有人意识到MS也是一种制备方法，例如它可用于将质量选择的离子沉积在表面上以创建新材料。从有机合成的角度来看，一个重要的应用是，MS可用于从反应混合物中产生微圆头，因此，通常通过数量级，可以将许多反应（尤其是在微螺旋体中）中的许多反应（尤其是在微副子中）相对于它们的散装速率加速。 DESI的一个独特特征是，用于分析反应混合物的溶剂喷雾在撞击时会产生次级液滴，而试剂可以在溶液相中反应，而液滴则将液滴携带到质谱仪。正是这种非凡的功能使DESI-MS成为强大的合成方法，并结合了内置的分析能力。 该协作项目包括几个虚拟模块的开发，以支持自主实验室中新分子的多步化学合成。这些模块旨在使用NCATS ASPIRE团队开发的集成硬件平台除了自动化化学家外，还可以使传统的合成化学家受益。从感兴趣的分子列表开始，计算机辅助综合计划可以看作是详细的层次结构过程：（1）回合合成计划，以识别合适的起始材料和中间体，（2）反应条件建议，以识别应运行每个反应步骤的条件，（3）假设反应序列在动作序列上可自动执行的序列可执行的硬件。可选但有价值的组件包括（4）通过实验计划模块记录程序，（5）通过平台的数字双胞胎对最有效合成多个合成目标的时机和动作顺序序列的优化，以及（6）基于反馈循环的实验响应的迭代过程优化。 化学仪器感知分布式区块链的开放AI平台以加速药物发现 人工智能（AI）和自动化有可能加速药物发现过程的几个阶段，包括通常由药物化学家面临的设计疗法测试分析的优化周期。但是，存在几个障碍，导致时间表过长，无法为未满足需求的患者提供急需的创新。人类和人工智能都面临类似的局限性，主要是由于获得和整合由不同组织或实验室生成的数据所需的脱节步骤，并且如果不披露IP敏感信息（例如，非养育的新型化学结构），就无法轻易共享。此外，缺乏公开可用的负面数据（失败）数据，这对于生成准确的AI模型至关重要，但由于与IP相关的各种原因，通常在始发机构或实验室之外没有可用。而且，即使在积极的结果中，也需要更大的协议可重复性。 在内部开发完全集成的系统的解决方案可能是有效的，但是很难扩展，并且不容易采用主要是由于涉及的成本和基础设施。我们的解决方案封装了NCATS ASPIRE计划的愿景，即集成和自动化实验室以加速药物发现过程，同时考虑到存在的上述问题。区块链是一种分布式分类帐技术，再加上AI和自动化，有可能解决上述所有问题，就像它在其他几个技术领域所做的那样，例如金融和医学，可以在没有透露其身份的情况下安全地共享和学习数据。我们将开发一个基于区块链的开放科学AI框架，作为药物发现社区的分散实验室云，以增强协作和可重复性。 提议作为模块化组件，可与NCAT的大规模自动合成计划相吻合，并与其他信息学工具（例如递归合成分析和库存管理）互操作。 中心技术创新是我们对深度神经网络进行图形转换的方法。在网络中间是一个化学丰富的向量（），它具有两个重要的特性：（1）它使用高度正交值的简短矢量捕获输入分子的结构多样性，使其对QSAR模型非常有效，而无需附加的描述符构图词； （2）可以将向量映射到分子空间以重新创建原始结构。