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）正在通过预测大型数据集的未来行为来彻底改变我们的世界。最近的兴奋围绕着AI的兴奋增长，需要小（<100）数据集，从而指导像药品这样的重要领域的投资。 “主动学习”（AL）技术使用实验结果，根据状态空间的区域不太确定其预测，为新实验设计提出建议。与AL相比，该模型试图找到给定目标概况的最佳材料，而与AL相比，该模型试图找到最佳的材料，该模型试图找到构建强大且可解释的模型。该项目将旨在开发一个廉价的机器人配方器，并通过AL-DREAKH决策来加速医学制造。可以预见的是，能够执行常规实验室任务的机器人，例如处理液体和进行分析测量，可以通过回归AL算法来指导它，以至于它不仅执行任务，而且还可以学习并执行下一个逻辑步骤，最终开发高质量，安全，有效的液体药物。整合AI，机器人技术和自动分析是一个巨大的挑战，但是结果可能是惊人的。机器人配方器可以使用较大的设计空间迅速通过质量数据来促进候选药物的候选药物，而浪费很少。这将在项目中通过挑战机器人的复杂药物来证明，这可能是未来的核心药物。可以预见的是，这种方法将能够鉴定出传统配方方法无法使用的药物和添加剂的复杂和不直觉的组合。预计该项目将对未来的创新产生逐步影响。与当前的机器人公式流相比，机器人配方器（例如材料创新工厂中使用的机器人公式流）相比，可以使用开源软件在标准PC上运行算法。这样，该方法可以在较低的资源环境中用于当地优先药物。及时对算法集成的重点是充分利用最近的回归原则，以及提出的蓝图适合AI的未来发展。为了实现该项目的雄心勃勃的目标，将遵循以下过程。首先，将指示廉价的液体处理机器人（9K，由PI拥有），以单个读出（例如吸收）来开发药物和添加剂（溶液中）的混合物。 XARM 5机器人臂将与液体处理机器人连接，以使公式可以转移到分析仪器中。然后，回归Al算法将分析哪些条件导致溶解度，并对机器人将自动研究的溶解度提高的公式进行预测。该过程将经过优化和评估，以证明机器人正在“学习”如何使这些药物更好。然后，这项研究将继续探索多种产品属性，类似于“现实世界”医学配方。该项目将匹配机器人执行的过程与行业所使用的过程，以确保在与拜耳合作的指导下，确保调查结果可以翻译。此外，该技术将设计用于使用行业标准的软件QBDVision，用于高质量处理和报告数据。这是机器人科学家还提供了完美的报告，以批准新药所需的结果。