Calibrated Weighted Rank Aggregation for Virtual Screening Independently Rediscovers Privileged Vitamin D Receptor...

Computational drug discovery relies on virtual screening to prioritize a small number of compounds for experimental testing, yet combining heterogeneous in silico scoring methods into a single robust ranking remains challenging. We present Calibrated Weighted Rank Aggregation (CWRA), a data-driven rank-optimized score-fusion framework that (a) direction-corrects modality outputs, (b) harmonizes them via per-modality normalization, and (c) learns nonnegative fusion weights from known actives by optimizing a rank-based early-recognition objective (Boltzmann-enhanced discrimination of receiver operating characteristic [BEDROC]) under simplex constraints. Applied to the vitamin D receptor (VDR), we fuse machine-learning affinity predictors (GraphDTA endpoints, MLT-LE, TankBind, DrugBAN, and MolTrans), structure-based scores (AutoDock Vina and Boltz-2 affinity and confidence), and ligand similarity (Uni-Mol; computed in a split-honest manner by recomputing the active-set centroid on the training splits). Across 5 repeated random splits over the $P=503$ known VDR binders, evaluated on the drug-likeness prefiltered pool of $N^{\text{'}}=14,902$ compounds, CWRA achieves strong early enrichment on held-out actives ( $\text{EF}@1\%=21.32\pm 6.41$ , corresponding to 16 ± 5 recovered binders within the top 1% of the ranked list), substantially outperforming equal-weight fusion ( $\mathrm{EF}@1\%=10.79\pm 2.81$ ) and the strongest single modality (GraphDTA IC ₅₀; $\text{EF}@1\%=16.32\pm 3.96$ ). Performance remains strong at broader cutoffs ( $\text{EF}@2.5\%=14.13\pm 2.34$ , $\text{EF}@5\%=9.47\pm 0.60$ , and $\text{EF}@10\%=7.13\pm 0.72$ ), indicating that BEDROC-optimized fusion of normalized scores improves both early prioritization and moderate-depth screening. To assess generalization beyond VDR, we further applied CWRA to the mechanistically distinct γ-aminobutyric acid type A (GABA_A) receptor. Target-specific calibration again improved very early enrichment ( $\text{EF}@1\%=25.41\pm 3.96$ ) over both equal-weight fusion ( $\text{EF}@1\%=17.47\pm 6.17$ ) and the best individual modality at this cutoff (Boltz-2 confidence; $\text{EF}@1\%=11.64\pm 2.12$ ). Strict cross-target source-model transfer between VDR and GABA_A was weaker than target-specific recalibration, indicating that CWRA should be interpreted as a transferable target-adaptive framework rather than a universal fixed-weight model. A qualitative review of the top-ranked generated candidates suggests that CWRA preferentially selects chemically coherent VDR-like chemotypes, recapitulating established VDR pharmacophore patterns without imposing handcrafted structural rules.