Artificial intelligence coupled to automation for accelerated medicine design

人工智能(AI)通过从大数据集中预测未来的行为，正在给我们的世界带来革命性的变化。最近，人们对人工智能越来越感兴趣，因为它需要较小的(100)数据集，指导制药等关键领域的调查。“主动学习”(AL)技术使用实验结果，根据状态空间中不太确定其预测的区域，为新的实验设计提供建议。这些预测的结果被输入到模型中，以供继续改进。对于材料发现任务，正在探索贝叶斯优化，但这一过程受到限制，因为与AL相比，模型试图在给定目标配置文件的情况下找到最佳材料，而AL专注于建立稳健和可解释的模型。该项目旨在开发一种廉价的具有AL驱动决策的机器人配方机，以加快药品生产。据设想，能够执行常规实验室任务的机器人，如处理液体和进行分析测量，可以由回归AL算法引导，以便它不仅执行任务，而且学习和执行下一个逻辑步骤，最终开发出高质量、安全和有效的液体药物。集成人工智能、机器人和自动分析是一个巨大的挑战，然而结果可能是惊人的。机器人配方设计师可以利用高质量的数据，利用巨大的设计空间，以极少的浪费，快速地推动候选药物通过制药瓶颈。这一点将在该项目中得到证明，用复杂的药物挑战机器人，这些药物很可能是未来的核心药物。据设想，这种方法将能够识别传统配方方法所不能识别的复杂和非直觉的药物和添加剂的组合。预计该项目将对未来的创新产生循序渐进的影响。与目前的机器人配方流(如材料创新工厂中使用的那些)相比，机器人配方器并不昂贵，而且算法可以在使用开源软件的标准PC上运行。因此，这种方法可以在当地优先药物的资源较少的环境中采用。算法集成的重点是及时最大限度地利用最新的回归AL原则，并提出了符合未来人工智能发展的蓝图。为了实现这一项目的宏伟目标，将遵循以下进程。首先，一个廉价的液体处理机器人(GB 9K，由PI拥有)将被指示开发药物和添加剂的混合物(在溶液中)，只需一次读出(例如吸光度)。Xarm 5机械臂将与液体处理机器人对接，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导致了溶解度，并生成对具有改善的溶解度的配方的预测，机器人将自动调查这些预测。这一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，这项研究将继续探索同时具有多种产品属性，类似于“现实世界”的药物配方。该项目将在与拜耳的合作指导下，将机器人执行的过程与行业使用的过程进行匹配，以确保结果可翻译。此外，该技术将被设计为使用行业标准软件QBDvision，以实现高质量的数据处理和报告。因此，机器人科学家还提供批准新药所需的无懈可击的结果报告。

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.