Computer Vision for Analytical Chemistry (CVAC): Scalable Productivity for Chemical Manufacturing

分析化学计算机视觉 (CVAC)：化学制造的可扩展生产力

• 批准号: MR/T043458/1
• 负责人: Marc Reid
• 金额: $ 120.16万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT043458%2F1
• 关键词: Computer; Vision; Analytical; Chemistry; CVAC

KEYWORDS: chemistry, catalysis, image processing, manufacturing, productivity, EPSRC.The digital eyes of cameras paint the colourful worlds we can and cannot see by numbers. These numbers have the power to help us make life-changing medicines on timescales that, at the present time, we cannot imagine.This research and leadership programme focusses on developing the analytical power of digital cameras to improve the productivity and safety of chemical manufacturing.The Pharmaceutical sector is the UK's second largest in terms of income, but it is an extremely costly business to run. This cost burden hits at the heart of one of the UK's biggest challenges: our lack of productive output versus hours worked compared with other nations. To this point, 'Big Pharma' stands to realise a >£1bn reduction in R&D cost by 2030, but only if the efficiency with which it can discover new medicines can be improved by a third beyond the current state of the art. How can we make new medicines more productively? Fast adoption of digital technologies is vital. As linked to the core of the proposed research, digital technology adoption should include the amazing ability of cameras to tell the story of the world, not in words, but in useful numbers.In Big Pharma, to understand whether or not the chemical process of making a medicine is safe to use on the manufacturing scale, we need to be able to analyse the chemical process in real time. The better we analyse a process on the small scale, the better its chances of being used productively to make medicines on the large scale. However, many useful reactions are never applied in industry because they do not meet the strict criteria for safe application on the manufacturing scale. This is an unsolved problem, and no current chemical monitoring technologies can seamlessly analyse chemical processes on small lab scale, large plant scale, and in dangerous environments. If such a monitoring technology were available, it has the potential to lead to an up-to 9:1 return on investment, moving us closer to the ultimate goal of improving research productivity by a full third.Computer Vision is the science of digitally quantifying real-world objects using cameras. It is a vibrant area of research with a rich history in astronomy, land surveys, autonomous systems, food safety, defence and security, and art forensics, among other areas. Whilst 'photo-style' camera analysis has been used over the past decade, new and unique methods of using real-time camera-based chemical monitoring is still hugely underdeveloped across chemical manufacturing, despite the wealth of emerging knowledge from seemingly unrelated scientific disciplines. The untapped technology of camera-enabled reaction monitoring thus holds remarkable fundamental research potential. A new research programme in this area would contribute strongly to UK chemical manufacturing, realising significant and digitally-adoptive increases in productivity 2-3 years ahead of current 2030 targets.This ambitious research programme will deliver a world-leading suite of new camera-enabled analytics for understanding a wide range of valuable chemical processes to make them safer and more productive on scale. The research leader has an emerging track record which has already directed step-changes in homogeneous catalyst design, reaction kinetics platforms, safety software systems, and industrial technology translation. Bordering chemistry and computer Science, this programme will deliver research excellence in video analysis methods for visible and invisible chemical processes, across all scales of chemical development, and in a wide range of chemistries beyond the core focus of improving productivity in Pharmaceutical development.

关键词：化学、催化、图像处理、制造、生产率、EPSRC。相机的数字眼睛描绘了我们可以用数字看到的五颜六色的世界，也不能用数字来描绘。这些数字有能力帮助我们在目前无法想象的时间尺度上制造改变生活的药物。这个研究和领导计划专注于开发数码相机的分析能力，以提高化学制造的生产率和安全性。就收入而言，制药行业是英国第二大行业，但这是一项极其昂贵的业务。这种成本负担触及了英国最大挑战之一的核心：与其他国家相比，我们缺乏生产率产出和工作时间。在这一点上，到2030年，“大型制药公司”将实现研发成本减少10亿英磅，但前提是该公司发现新药的效率能够在当前技术水平基础上提高三分之一。我们怎样才能使新药更有成效呢？快速采用数字技术至关重要。作为拟议研究的核心，数字技术的采用应该包括相机惊人的能力来讲述世界的故事，不是用语言，而是用有用的数字。在大型制药公司，要了解制造药物的化学过程在生产规模上是否安全，我们需要能够实时分析化学过程。我们越好地在小规模上分析一个过程，它被用于大规模生产药物的机会就越大。然而，许多有用的反应从未在工业上应用，因为它们不符合在生产规模上安全应用的严格标准。这是一个悬而未决的问题，目前还没有一种化学监测技术可以无缝地分析小实验室、大工厂和危险环境中的化学过程。如果有这样的监控技术，它有可能带来高达9：1的投资回报，使我们更接近将研究效率提高三分之一的最终目标。计算机视觉是使用相机对现实世界的物体进行数字量化的科学。它是一个充满活力的研究领域，在天文学、土地调查、自主系统、食品安全、国防和安全以及艺术法医等领域有着丰富的历史。虽然在过去十年中已经使用了“照片式”的相机分析，但在整个化学制造业中，使用基于相机的实时化学监测的新的和独特的方法仍然非常不发达，尽管来自看似无关的科学学科的新兴知识非常丰富。因此，尚未开发的相机使能反应监测技术具有显着的基础研究潜力。这一领域的一项新研究计划将有力地促进英国的化学制造业，比目前的2030年目标提前2-3年实现生产率的显著和数字采用的提高。这一雄心勃勃的研究计划将提供一套世界领先的新的相机支持的分析，以了解广泛的有价值的化学过程，使其更安全和更具规模的生产力。这位研究领先者拥有新兴的记录，已经在均质催化剂设计、反应动力学平台、安全软件系统和工业技术转换方面指导了阶段性变化。该计划与化学和计算机科学接壤，将在可见和不可见化学过程的视频分析方法方面提供卓越的研究成果，涉及所有化学开发规模，以及超出提高制药开发生产率这一核心重点的广泛化学领域。

项目成果

Computer Vision as a New Paradigm for Monitoring of Solution and Solid Phase Peptide Synthesis

计算机视觉作为溶液和固相肽合成监测的新范式

• DOI: 10.26434/chemrxiv-2023-tp5n9
• 发表时间: 2023
• 作者: Yan C

Teaching Old Presumptive Tests New Digital Tricks with Computer Vision for Forensic Applications

利用计算机视觉在法医应用中教授旧的推定测试新的数字技巧

• DOI: 10.26434/chemrxiv-2023-04d1g
• 发表时间: 2023
• 作者: Bugeja N

Computer Vision for Kinetic Analysis of Lab- and Process-Scale Mixing Phenomena.

• DOI: 10.1021/acs.oprd.2c00216
• 发表时间: 2022-11-18
• 期刊: ORGANIC PROCESS RESEARCH & DEVELOPMENT
• 影响因子: 3.4
• 作者: Barrington, Henry;Dickinson, Alan;McGuire, Jake;Yan, Chunhui;Reid, Marc
• 通讯作者: Reid, Marc

Computer Vision for Kinetic Analysis of Lab- and Process-scale Mixing Phenomena

用于实验室和过程规模混合现象动力学分析的计算机视觉

• DOI: 10.26434/chemrxiv-2022-3911h
• 发表时间: 2022
• 作者: Barrington H

Computer Vision for Understanding Catalyst Degradation Kinetics

用于了解催化剂降解动力学的计算机视觉

• DOI: 10.26434/chemrxiv-2022-n0wf3
• 发表时间: 2022
• 作者: Yan C