Reliability-Calibrated Pose-by-Metric Fusion for Target-Local Virtual Screening

Structure-based virtual screening ranks candidate ligands against a target protein by aggregating heterogeneous evidence from predicted protein-ligand complexes, structural confidence, docking affinity, interface geometry, interaction fingerprints, and ligand descriptors, yet within-target ranking quality remains decisive for downstream hit rates. Two difficulties complicate this aggregation: binding is pose-dependent with uncertain pose quality at inference time, and supervised ranking signals are sharply non-uniform across targets, with metric reliability also varying per target. We propose **PoT-Rank** (**Po**se-**T**rust Ranking), a target-local learning-to-rank framework that represents each candidate as a masked pose-by-metric tensor and learns hierarchical softmax weights over feature suites, metrics, and poses. Metric trust is calibrated without label leakage through a training-only reliability gate based on same-target pairwise concordance, paired with a target-normalized hard-decoy curriculum. To address differing statistical risks, PoT-Rank splits into a dense pose-aware ranker with a target-balanced pairwise hinge loss and a sparse antisymmetric comparator with signed-vote synthesis, with all stateful components frozen before evaluation. Under a unified frozen protocol with protein-side MMseqs2 splits at 40% identity, PoT-Rank attains the strongest within-target ranking in both regimes, reaching within-target pairwise accuracy 0.6602 and Spearman 0.5343 on the dense split (3 targets, 565 within-target pairs) and 0.6897 and 0.4313 on the sparse split (79 targets). It surpasses classical scoring functions, deep affinity predictors, graph-based models, pose-aware rescorers, and structure-prediction-based affinity models, and matches or exceeds strong tabular baselines in both regimes, while remaining stable under leakage audits, multi-seed perturbation, and corrupted-channel stress tests. Our code is available at https://anonymous.4open.science/r/PoT-Rank-4CD1/.