Conformal Prediction and Multi-Ensemble Gradient Boosting for Subtype-Selective Binding Affinity Estimation Across Human Adenosine Receptors

Abstract

Subtype selectivity across human adenosine GPCRs remains an intractable medicinal chemistry challenge. The four orthosteric binding pockets share over 70% sequence homology across transmembrane helices III, V, VI, and VII. Consequently, standard QSAR models fail prospectively. They suffer from systemic scaffold leakage, overestimating held-out affinity while producing point estimates that lack calibrated error bars. We built an open-source, leak-free computational platform to solve both failure modes. The architecture combines XGBoost gradient boosting with MAPIE Jackknife+ cross-conformal prediction, Random Forest, and LightGBM, trained on 9,589 curated ChEMBL v34 and GPCRdb bioactivity records. Under strict Bemis-Murcko scaffold partitioning (N_train = 6,332; N_test = 1,583), the ensemble achieved an overall R2 of 0.693 and MAE of 0.390 pChEMBL units. On active compounds alone (N_test = 3,771, structural decoys removed), accuracy reached an overall R2 of 0.865 and MAE of 0.314. Per-subtype R2 values reached 0.753 for A1, 0.884 for A2A, 0.912 for A2B, and 0.886 for A3. Conformal intervals delivered 85.80% empirical coverage at a 90% nominal confidence level. Uncertainty quartiles scaled monotonically with absolute prediction error. A GINE graph neural network trained on identical scaffold splits managed only R2 = 0.248 overall, demonstrating that curated physicochemical descriptors decisively outperform deep graph convolutions in low-to-medium data regimes. Twenty-fold Y-randomization confirmed genuine structure-activity relationships, with all permuted R2 values falling below zero (p < 0.001). External blind validation on 15 novel GPCRdb ligands yielded a 75% selectivity recall accuracy. TreeSHAP features attributions verified that model decisions follow interpretable electrostatic and steric properties. All source code, curated data splits, model weights, and interactive deployment are publicly available.