Chemical instruments-aware distributed blockchain based open AI platform to accelerate drug discovery

基于化学仪器感知的分布式区块链开放人工智能平台，加速药物发现

• 批准号: 10448092
• 负责人: GAURAV CHOPRA
• 金额: $ 50.21万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10448092
• 关键词: Academia; Adopted; Affect; Affinity; Artificial Intelligence; Artificial Intelligence platform; Automation; Awareness; Binding; Biological; Chemical Structure; Chemicals; Chemistry; Code; Collaborations; Collection; Communities; Complex; Computer software; Coupled; Data; Data Analyses; Data Set; Databases; Decentralization; Development; Diagnostic; Disclosure; Encapsulated; Face; Failure; Feedback; Generations; Government; High Pressure Liquid Chromatography; Human; Information Technology; Infrastructure; Institution; Interruption; Ions; Joints; Laboratories; Learning; Location; Manuals; Measurement; Medicine; Modeling; Molecular; Molecular Structure; National Center for Advancing Translational Sciences; Outcome; Patients; Performance; Process; Property; Protocols documentation; Publishing; Reaction; Reproducibility; Research; Research Proposals; Resources; Schedule; Scientist; Secure; Site; Standardization; System; Techniques; Technology; Testing; Time; TimeLine; Training; Vision; Work; application programming interface; automated analysis; base; blockchain; computing resources; cost; cyber infrastructure; design; distributed data; distributed ledger; drug candidate; drug discovery; experience; experimental study; graphical user interface; innovation; instrument; learning algorithm; learning progression; novel; open data; open source; programs; screening; tool; virtual

PROJECT SUMMARY 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. This includes decentralized performance of experiments and enabling efficient multi-party analysis and learning on remote datasets using application programming interface (API) and graphical user interface (GUI) to engage both computational and experimental scientists. Continuous learning from measurements and accessible data (with GUI for humans and API for machines) will enable unprecedented reproducibility. Our decentralized blockchain network will be interfaced across four sites (Purdue, IBRI, IU and NCATS) in a modular manner that is extendable to large scale to accommodate several thousand laboratories without affecting efficiency at scale. Our network solution is modular, in that, it works with multiple resources (instruments, databases) either within one laboratory or across multiple laboratories and organizations. This distributed blockchain network will enable secure multi-party joint training of AI model on databases at different locations (cloud instances, IU and NCATS) and schedule experiments with a physical instrument (Purdue) with data interpretation and secure sharing of results to enhance the efficiency of cross-organizational and collaborative research. The integrated API and GUI will be developed by utilizing core technology components from the NCATS Information Technology Resource Branch to facilitate transfer and integration with the ASPIRE platform. Collectively, this open-source solution will enhance cycle times to accelerate drug discovery research.

项目总结 人工智能(AI)和自动化有可能加速药物发现的几个阶段 过程，包括设计-制造-测试-分析优化循环，这通常是药物化学家面临的。 然而，存在几个障碍，导致时间太长，无法向患者提供急需的创新 有未得到满足的需求。人类和人工智能都面临着类似的限制，主要是因为需要分离的步骤来获得 并将不同组织或实验室生成的、不容易共享的数据进行集成 而不披露知识产权敏感信息(例如，非专利的新型化学结构)。此外，还有 缺乏公开的负面(失败)数据，这些数据对于生成准确的人工智能模型至关重要，但 由于各种相关原因，通常不能在发起机构或实验室之外提供 转到IP。而且，即使是在积极的结果中，方案的更大的重复性也是可取的。一种开发一种 完全集成的内部系统可能很有效，但很难扩展，也不容易采用，主要原因是 所涉及的成本和基础设施。我们的解决方案概括了NCATS ASPIRE计划集成的愿景 以及使实验室自动化，以加快药物发现过程，同时考虑到上述情况 存在的问题。区块链，一种分布式账本技术，再加上AI和自动化，拥有 有可能解决所有上述问题，就像它在金融等其他几个技术部门所做的那样 以及在不透露身份的情况下安全地共享和学习数据的医学。我们将开发区块链 以开放科学AI框架为基础，为药物发现社区提供分散的实验室云 增强协作性和再现性。这包括分散执行实验和启用 使用应用编程接口(API)对远程数据集进行高效的多方分析和学习 图形用户界面(GUI)，让计算科学家和实验科学家都参与进来。持续学习 来自测量和可访问的数据(通过用于人类的图形用户界面和用于机器的API)将实现前所未有的 再现性。我们的去中心化区块链网络将跨四个站点(普渡大学、IBRI、伊利诺伊大学和 NCATS)采用模块化方式，可大规模扩展以容纳数千个实验室 而不影响规模化效率。我们的网络解决方案是模块化的，因为它可以使用多种资源 (仪器、数据库)在一个实验室内或跨多个实验室和组织。这 分布式区块链网络将实现AI模型在不同数据库上的安全多方联合训练 地点(云实例、Iu和NCATS)并安排使用物理仪器(普渡)进行实验 数据解释和成果安全共享，以提高跨组织和 合作研究。将利用核心技术组件开发集成的API和图形用户界面 来自NCATS信息技术资源处，以促进与ASPIRE的转移和整合 站台。总而言之，这一开源解决方案将缩短周期，加快药物发现研究。