Projects per year
Abstract
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.
Original language | English |
---|---|
Article number | 100 |
Pages (from-to) | 1-9 |
Number of pages | 9 |
Journal | JOURNAL OF CHEMINFORMATICS |
Volume | 16 |
Issue number | 1 |
DOIs | |
Publication status | Published - Dec 2024 |
MoE publication type | A1 Journal article-refereed |
Keywords
- De novo drug design
- Human-in-the-loop
- Machine learning
- Preference learning
- User interface
Fingerprint
Dive into the research topics of 'Metis : a python-based user interface to collect expert feedback for generative chemistry models'. Together they form a unique fingerprint.-
MSCA AIDD /Kaski S.: Advanced machine learning for Innovative Drug Discovery
Kaski, S. (Principal investigator), Masood, A. (Project Member) & Nahal, Y. (Project Member)
01/01/2021 → 31/12/2024
Project: EU: MC
-
-: Finnish Center for Artificial Intelligence
Kaski, S. (Principal investigator)
01/01/2019 → 31/12/2022
Project: Academy of Finland: Other research funding