When Your Model Does Not Know What It Does Not Know: A Drug Discovery ML Project
📰 Medium · Python
Learn to build a molecular binding affinity prediction pipeline with Graph Attention Networks and uncertainty quantification to improve drug discovery
Action Steps
- Build a molecular binding affinity prediction pipeline using Graph Attention Networks
- Implement MC Dropout for uncertainty quantification
- Configure the pipeline to handle missing data and uncertainty
- Test the pipeline on a drug discovery dataset
- Apply the pipeline to predict molecular binding affinities for new compounds
Who Needs to Know This
Data scientists and machine learning engineers working on drug discovery projects can benefit from this pipeline to improve the accuracy of their predictions
Key Insight
💡 Uncertainty quantification is crucial in drug discovery to identify when a model is uncertain about its predictions
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🧬 Improve drug discovery with Graph Attention Networks and uncertainty quantification! #ML #DrugDiscovery
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