Artificial intelligence coupled to automation for accelerated medicine design

人工智能（AI）正在通过从大型数据集预测未来行为来彻底改变我们的世界。最近，人们对人工智能的兴趣越来越大，它需要小规模（<100）的数据集，指导对制药等重要领域的研究。“主动学习”（AL）技术使用实验结果，根据状态空间中对其预测不太确定的区域为新的实验设计提出建议。这些预测的结果将输入到模型中，以进行持续改进。贝叶斯优化正在探索用于材料发现任务，然而，与AL相比，该过程是有限的，因为模型试图找到给定目标配置文件的最佳材料，而AL专注于构建一个强大且可解释的模型。该项目的目标是开发一种廉价的机器人配方师，具有人工智能驱动的决策，以加速药品生产。可以设想，能够执行常规实验室任务（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，使得它不仅执行任务，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。整合人工智能、机器人技术和自动化分析是一个巨大的挑战，但结果可能是惊人的。机器人配方设计师可以利用高质量的数据，使用大的设计空间，几乎没有浪费，快速驱动候选药物通过制药瓶颈。这将在该项目中通过挑战机器人的复杂药物来证明，这些药物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无法识别的药物和添加剂的复杂和不直观的组合。预计该项目将对未来的创新产生逐步的影响。与当前的机器人配方流（例如材料创新工厂中使用的那些）相比，机器人配方师价格低廉，并且算法可以使用开源软件在标准PC上运行。因此，这种方法可以在资源较少的地方优先药物环境中采用。算法集成的重点及时充分利用最近的回归人工智能的原则，并提出了适合未来发展的蓝图在人工智能。为了实现该项目的宏伟目标，将遵循以下程序。首先，一个便宜的液体处理机器人（9000英镑，由PI拥有）将被指示开发药物和添加剂的混合物（在溶液中），具有单一的读数（例如吸光度）。Xarm 5机械臂将与液体处理机器人连接，以便将制剂转移到分析仪器中。然后，回归AL算法将分析哪些条件导致溶解度，并生成机器人将自动调查的具有改善溶解度的配方的预测。这个过程将被优化和评估，以证明机器人正在“学习”如何使这些药物更好。然后，研究将继续探索多种产品属性，类似于“真实的世界”的药物配方。该项目将使机器人执行的过程与工业使用的过程相匹配，以确保在与拜耳合作的指导下，研究结果是可翻译的。此外，该技术将使用行业标准软件QBDvision进行高质量的数据处理和报告。因此，机器人科学家还提供了批准新药所需的完美结果报告。

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.