One challenge that current de novo drug design models face is a disparity between the user’s expectations and the actual output of the model in practical applications. Tailoring models to better align with chemists’ implicit knowledge, expectation and preferences is key to overcoming this obstacle effectively. While interest in preference-based and human-in-the-loop machine learning in chemistry is continuously increasing, no tool currently exists that enables the collection of standardized and chemistry-specific feedback. Metis is a Python-based open-source graphical user interface (GUI), designed to solve this and enable the collection of chemists’ detailed feedback on molecular structures. The GUI enables chemists to explore and evaluate molecules, offering a user-friendly interface for annotating preferences and specifying desired or undesired structural features. By providing chemists the opportunity to give detailed feedback, allows researchers to capture more efficiently the chemist’s implicit knowledge and preferences. This knowledge is crucial to align the chemist’s idea with the de novo design agents. The GUI aims to enhance this collaboration between the human and the “machine” by providing an intuitive platform where chemists can interactively provide feedback on molecular structures, aiding in preference learning and refining de novo design strategies. Metis integrates with the existing de novo framework REINVENT, creating a closed-loop system where human expertise can continuously inform and refine the generative models. Scientific contribution We introduce a novel Graphical User Interface, that allows chemists/researchers to give detailed feedback on substructures and properties of small molecules. This tool can be used to learn the preferences of chemists in order to align de novo drug design models with the chemist’s ideas. The GUI can be customized to fit different needs and projects and enables direct integration into de novo REINVENT runs. We believe that Metis can facilitate the discussion and development of novel ways to integrate human feedback that goes beyond binary decisions of liking or disliking a molecule.

中文翻译：

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Metis：基于 Python 的用户界面，用于收集生成化学模型的专家反馈


当前的从头药物设计模型面临的挑战之一是用户的期望与模型在实际应用中的实际输出之间的差异。定制模型以更好地符合化学家的隐性知识、期望和偏好是有效克服这一障碍的关键。尽管人们对化学中基于偏好和人机交互的机器学习的兴趣不断增加，但目前还没有工具可以收集标准化和特定于化学的反馈。 Metis 是一个基于 Python 的开源图形用户界面 (GUI)，旨在解决这个问题并能够收集化学家对分子结构的详细反馈。 GUI 使化学家能够探索和评估分子，提供用户友好的界面来注释偏好并指定所需或不需要的结构特征。通过为化学家提供提供详细反馈的机会，研究人员可以更有效地捕获化学家的隐性知识和偏好。这些知识对于将化学家的想法与从头设计代理保持一致至关重要。 GUI 旨在通过提供一个直观的平台来增强人类与“机器”之间的协作，化学家可以在该平台上交互式地提供有关分子结构的反馈，帮助偏好学习和完善从头设计策略。 Metis 与现有的 de novo 框架 REINVENT 集成，创建了一个闭环系统，人类的专业知识可以不断地为生成模型提供信息和完善。科学贡献我们引入了一种新颖的图形用户界面，允许化学家/研究人员提供有关小分子的子结构和性质的详细反馈。 该工具可用于了解化学家的偏好，以便使从头药物设计模型与化学家的想法保持一致。 GUI 可以定制以满足不同的需求和项目，并能够直接集成到 de novo REINVENT 运行中。我们相信 Metis 可以促进整合人类反馈的新方法的讨论和开发，超越喜欢或不喜欢分子的二元决定。