Researchers have identified a significant issue with marginal conformal prediction, a method used in drug discovery to quantify model reliability. The study reveals that on imbalanced datasets, this method fails to provide adequate coverage for minority classes, exposing them to a higher risk of misclassification. This problem persists across various model architectures, including random forests, graph networks, and chemical language models. A proposed class-conditional conformal prediction method effectively addresses this under-coverage, restoring reliability to minority classes and improving the overall utility of screening campaigns. AI
IMPACT Highlights a critical flaw in AI reliability for drug discovery, potentially impacting safety and efficiency of screening processes.
RANK_REASON The cluster contains an academic paper detailing a novel finding and proposed solution in a specific machine learning application.
- benzene
- Chemical language models for de novo drug design: Challenges and opportunities
- Class-Conditional Conformal Prediction
- Graph Networks for Materials Exploration
- Marginal Conformal Prediction
- Muhammadjon Tursunbadalov
- pyridine
- random forest
- Conformal prediction
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