Representation-aware conformal prediction for reliable uncertainty quantification in drug-target affinity.

Representation-aware conformal prediction framework RCP-DTA achieves near-nominal coverage with sample-specific intervals in drug-target affinity prediction.

• Why it matters: Reliable uncertainty quantification in drug-target affinity models is crucial to reduce experimental costs and false positives, yet most models lack this feature.
• What they did: The authors developed RCP-DTA, which uses local calibration sets and a cross-modal architecture integrating sequence and structural data to generate adaptive prediction intervals based on feature similarity.
• The result: RCP-DTA maintains competitive affinity prediction accuracy while providing empirically calibrated uncertainty estimates, enabling more reliable decision-making in drug discovery.