mims-harvard/Madrigal
Madrigal: Multimodal AI predicts clinical outcomes of drug combinations from preclinical data
Madrigal helps drug discovery researchers and pharmacologists predict the clinical outcomes of drug combinations by analyzing multimodal preclinical data. It takes in various types of drug data, such as molecular structures, gene expression profiles, and cell viability assays, and outputs predictions about how two drugs will interact. This is ideal for scientists exploring new drug combinations or evaluating potential drug-drug interactions in early-stage development.
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Use this if you need to predict the effects and potential outcomes of combining different drugs based on a variety of preclinical data sources.
Not ideal if you are looking for a simple, off-the-shelf application for immediate clinical decision support without adaptation or a deep understanding of machine learning workflows.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 31, 2025
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