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technologyreview.com·1h ago
AI Research
arXiv CS.AI·15d ago·5 min read
Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion
TL;DR
Predicting protein-ligand binding affinities accurately is challenging due to variability in data sources. A novel multi-engine fusion approach was developed, integrating predictions from multiple models while accounting for their reliability.
✦ Why It Matters
Researchers can adopt the reliability-aware multi-engine fusion method to enhance the accuracy of their binding affinity predictions today.
Key Takeaways
- Utilize RELIABLE-BA for improved binding affinity predictions.
- Incorporate uncertainty measures to filter low-confidence predictions.
- Leverage multi-engine fusion for better consensus in predictions.
- Apply the framework to clinically relevant drug targets.
How It Works
RELIABLE-BA models each docking engine as an evidential expert, using Normal-Inverse-Gamma distributions to represent their predictions. It learns reliability from the molecular context, allowing for a nuanced scaling of epistemic uncertainty.
The framework then fuses these expert predictions through a closed-form aggregation that captures both individual uncertainties and the disagreements among the engines.