Artificial intelligence coupled to automation for accelerated medicine design

人工智能与自动化相结合，加速药物设计

• 批准号: EP/Z533038/1
• 负责人: Michael Cook
• 金额: $ 19.11万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FZ533038%2F1
• 关键词: Artificial; intelligence; coupled; automation; accelerated

Artificial intelligence (AI) is revolutionizing our world by predicting future behaviours from large datasets. Recent excitement has grown around AI that requires small (<100) datasets, guiding the investigation of vital areas like pharmaceuticals. "Active learning" (AL) techniques use experiment outcomes to make recommendations for new experiment designs based on areas of the state space where it is less certain of its predictions. The results of these predictions feed into the model for continual improvement. Bayesian Optimisation is being explored for material discovery tasks, however this process is limited in that models attempt to find the optimal material given a target profile, compared to AL, which focusses on building a robust and interpretable model. This project will aim to develop an inexpensive robot formulator with AL-driven decision-making to accelerate medicine manufacture. It is envisioned that a robot that is able to perform routine laboratory tasks, such as handling liquids and taking analytical measurements, could be guided by a regression AL algorithm such that it not only performs tasks, but learns and executes the next logical step, ultimately developing high quality, safe, and efficacious liquid medicines. Integrating AI, robotics, and automated analysis is an enormous challenge, however the outcomes could be phenomenal. Robotic formulators could drive drug candidates through pharmaceutical bottlenecks rapidly with quality data, using a large design space, with little waste. This will be demonstrated in the project by challenging the robot with complex drugs which are likely to be core medicines of the future. It is envisioned that this approach will be able to identify complex and unintuitive combinations of drug and additives which traditional formulation approaches would not.It is anticipated that the project will have step-wise impact on future innovations. The robot formulator is inexpensive in comparison to current robotic formulation streams (such as those used in the Materials Innovation Factory) and the algorithms can be run on standard PCs using open-source software. Thus, the approach can be adopted in lower-resource environments for local priority medicines. The focus on algorithm integration timely to make best use of recent regression AL principles, and the blueprint proposed amenable to future developments in AI. In order to achieve the ambitious aims of this project, the following process will be followed. Firstly, an inexpensive liquid-handling robot (£9k, owned by the PI) will be instructed to develop mixtures of drug and additive (in solution) with a single read-out (e.g. absorbance). An Xarm 5 robotic arm will be interfaced with the liquid-handling robot to allow the formulations to be transferred into analytical instruments. A regression AL algorithm will then analyse which conditions led to solubility and generate predictions on formulations with improved solubility that the robot will automatically investigate. This process will be optimised and evaluated to demonstrate that the robot is "learning" how to make these medicines better. The study will then move on to exploration of multiple product attributes at the same time, akin to "real world" medicine formulation. The project will match processes the robot performs to those used by industry, to ensure the findings are translatable, guided by collaboration with Bayer. Furthermore, the technology will be designed to use industry-standard software, QBDvision, for high-quality handling and reporting of data. Thus, the robot scientist also provides immaculate reporting of results that are needed for approval of new medicines.

人工智能 (AI) 通过从大型数据集预测未来行为，正在彻底改变我们的世界。最近，人们对需要小型（<100）数据集的人工智能越来越感兴趣，它可以指导“主动学习”（AL）技术等重要领域的研究。实验结果，根据其预测不太确定的状态空间区域提出新实验设计的建议。这些预测的结果将输入到模型中，以实现材料发现任务的持续改进，然而，与 AL 相比，该过程的局限性在于模型试图在给定目标轮廓的情况下找到最佳材料，而 AL 侧重于构建稳健且可解释的模型，该项目旨在开发具有 AL 驱动决策的廉价机器人配方师。可以预见，能够执行日常实验室任务（例如处理液体和进行分析测量）的机器人可以由回归 AL 算法引导，这样它不仅可以执行任务，还可以学习和执行任务。下一个合乎逻辑的步骤，最终开发高质量，集成人工智能、机器人技术和自动化分析是一项巨大的挑战，但机器人配方师可以利用大量的设计空间，利用高质量的数据，快速突破制药瓶颈，并且几乎不会造成浪费。该项目将通过使用可能成为未来核心药物的复杂药物来挑战机器人来证明这一点，预计这种方法将能够识别传统配方方法所无法识别的复杂且不直观的药物和添加剂组合。预计该项目将对未来的创新产生逐步影响，与当前的机器人配方流程（例如材料创新工厂中使用的流程）相比，机器人配方师的成本较低，并且算法可以在标准 PC 上运行。因此，该方法可以在资源匮乏的环境中用于本地优先药物。及时关注算法集成，以充分利用最新的回归 AL 原理，并提出适合人工智能未来发展的蓝图。为了实现雄心勃勃的目标在该项目中，将遵循以下过程：首先，将指示一个廉价的液体处理机器人（9000 英镑，由 PI 拥有）开发具有单一读数（例如吸光度）的药物和添加剂（在溶液中）的混合物。 Xarm 5 机械臂将与液体处理机器人连接，以便将配方转移到分析仪器中，然后回归 AL 算法将分析哪些条件会导致溶解度并生成改进的配方预测。机器人将自动研究溶解度，该过程将被优化和评估，以证明机器人正在“学习”如何使这些药物变得更好，然后该研究将继续探索多种产品属性，类似于。该项目将在与拜​​耳的合作指导下，将机器人执行的流程与行业使用的流程进行匹配，以确保研究结果可转化。此外，该技术将设计为使用行业标准软件 QBDvision。用于高质量处理和因此，机器人科学家还可以提供批准新药所需的完美结果报告。