ConfDock: Atom-specific Uncertainty Quantification for Molecular Docking via Conformal Prediction

Abstract

Molecular docking is widely used in structure-based drug discovery, yet most approaches provide point estimates without rigorous uncertainty quantification. This limitation makes it difficult to assess when a predicted pose should be trusted, especially when docking methods are applied to diverse protein–ligand systems. We present ConfDock, a conformal prediction (CP) framework for constructing atom-specific prediction intervals for ligand docking poses. ConfDock combines graph neural network (GNN) based quantile estimation with split conformal calibration, producing intervals that adapt to local protein–ligand environments while retaining distribution-free finite-sample coverage guarantees. We evaluate ConfDock on 238 protein–ligand complexes across four docking methods representing distinct computational paradigms. The proposed approach yields substantially narrower prediction intervals compared to standard split CP (57.2% average reduction in mean interval width, up to 74.5%) while maintaining target coverage across all evaluated settings. Ablation analysis indicates that the GNN captures the dominant structure-dependent variability in uncertainty, whereas the conformal calibration step provides a bounded adjustment to ensure coverage guarantees. These results demonstrate that combining learned, structure-aware quantile estimation with conformal calibration enables rigorous uncertainty quantification for molecular docking at atom-level resolution.