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Metis: a python-based user interface to collect expert feedback for generative chemistry models.

Janosch Menke1, Yasmine Nahal2, Esben Jannik Bjerrum3

  • 1Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 41296, Sweden. janosch.menke.research@proton.me.

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|August 14, 2024
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Summary
This summary is machine-generated.

Metis, a new GUI tool, collects chemists' detailed feedback on molecules to improve de novo drug design. This human-in-the-loop approach aligns AI models with expert chemical preferences.

Keywords:
De novo drug designHuman-in-the-loopMachine learningPreference learningUser interface

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Machine learning

Background:

  • Current de novo drug design models struggle to align with chemists' implicit knowledge and preferences.
  • Existing methods lack standardized tools for collecting chemistry-specific feedback on molecular structures.

Purpose of the Study:

  • To introduce Metis, an open-source Python GUI for collecting detailed chemical feedback.
  • To enable preference learning for refining de novo drug design models.
  • To facilitate human-in-the-loop machine learning in drug discovery.

Main Methods:

  • Developed a user-friendly graphical user interface (GUI) for exploring and evaluating molecules.
  • Implemented features for annotating preferences and specifying desired/undesired structural features.
  • Integrated Metis with the REINVENT de novo design framework for a closed-loop system.

Main Results:

  • Metis successfully captures detailed chemists' feedback on molecular structures and properties.
  • The GUI allows for efficient collection of implicit chemical knowledge.
  • Demonstrated integration with REINVENT for continuous model refinement.

Conclusions:

  • Metis enhances collaboration between chemists and de novo design agents by capturing nuanced preferences.
  • This tool facilitates the development of advanced preference learning strategies in drug design.
  • Metis moves beyond binary feedback, enabling richer human-AI interaction for improved molecular design.