Menke Janosch, Nahal Yasmine, Bjerrum Esben Jannik, Kabeshov Mikhail, Kaski Samuel, Engkvist Ola
Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 41296, Sweden.
Department of Computer Science, Aalto University, Espoo, 02150, Finland.
J Cheminform. 2024 Aug 14;16(1):100. doi: 10.1186/s13321-024-00892-3.
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 contributionWe 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.
当前从头设计药物的模型面临的一个挑战是，在实际应用中，用户期望与模型的实际输出之间存在差距。使模型更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。虽然化学领域对基于偏好和人工参与的机器学习的兴趣不断增加，但目前还没有能够收集标准化且特定于化学领域反馈的工具。Metis是一个基于Python的开源图形用户界面（GUI），旨在解决这一问题，并能够收集化学家对分子结构的详细反馈。该GUI使化学家能够探索和评估分子，提供一个用户友好的界面来标注偏好并指定所需或不需要的结构特征。通过为化学家提供给出详细反馈的机会，使研究人员能够更有效地捕捉化学家的隐性知识和偏好。这些知识对于使化学家的想法与从头设计代理保持一致至关重要。该GUI旨在通过提供一个直观的平台来加强人与“机器”之间的这种协作，在这个平台上，化学家可以交互式地提供关于分子结构的反馈，有助于偏好学习和改进从头设计策略。Metis与现有的从头设计框架REINVENT集成，创建了一个闭环系统，在这个系统中，人类专业知识可以不断为生成模型提供信息并对其进行优化。
科学贡献
我们引入了一种新颖的图形用户界面，它允许化学家/研究人员对小分子的子结构和性质给出详细反馈。这个工具可用于了解化学家的偏好，以便使从头设计药物的模型与化学家的想法保持一致。该GUI可以定制以适应不同的需求和项目，并能够直接集成到从头REINVENT运行中。我们相信，Metis可以促进关于整合人类反馈的新方法的讨论和开发，这种方法超越了对分子喜欢或不喜欢的二元决策。
