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.