Quantum annealer-based molecular docking approaches can be integrated with machine learning pipelines to enhance the prediction of molecular interactions in Multiple Sclerosis transcriptomics data.

Applying a quantum-annealer-based molecular docking pipeline (e.g., D-Wave Advantage QUBO formulation of ligand-pocket conformational search) to score candidate small-molecule/inhibitor binding against the three prioritized MS-CA-RIM targets (CTSS, ZNF740/BRD3 BET-bromodomain pocket, DNMT1 catalytic domain) — when the resulting quantum-derived docking scores are fused as features into a classical ML re-scoring model (gradient-boosted trees or graph neural network trained on ChEMBL bioactivity data) — will improve binding-affinity rank-correlation (Spearman ρ) against experimental pChEMBL/IC50 ground truth by ≥10% relative to a classical-docking-only baseline (e.g., AutoDock Vina/Glide SP), on a held-out test set of ≥200 compounds per target, at equivalent or lower wall-clock cost per compound.

Minimum viable test (single target, CTSS, as most druggable/best-characterized with >100 ChEMBL ligands, pChEMBL up to 10.0):

1. Curate n=200-300 CTSS ligands from ChEMBL with resolved pChEMBL values and split 70/15/15 (train/val/test), stratified by scaffold (Bemis-Murcko) to prevent leakage.
2. Generate classical docking baseline: AutoDock Vina/Smina against CTSS crystal structure (PDB 4P6D or best available apo/holo), 3 independent seeds per ligand.
3. Generate quantum-annealer docking scores: formulate ligand pose/rotamer search as QUBO (e.g., using D-Wave Ocean SDK + a docking-QUBO framework such as those built on Qubit-based combinatorial pose optimization), run on D-Wave Advantage2 (or hybrid solver as fallback given qubit constraints), 100 anneal cycles per ligand.
4. Train classical ML re-scorer (XGBoost or GNN, e.g., using RDKit descriptors + docking score as features) on train split; separately train fused model with both classical and quantum docking scores.
5. Evaluate all three models (classical-only, quantum-only, fused) on held-out test set via Spearman ρ, RMSE, and AUC (active/inactive at pChEMBL≥6.5 threshold).
6. Repeat for BRD3 bromodomain (using JQ1/birabresib/mivebresib/pelabresib-class ligands, n150-200 from ChEMBL) and DNMT1 (azacitidine/decitabine analogs + broader DNMT1 ChEMBL set, n100-150).
7. Statistical comparison via bootstrap resampling (n=1000) of ρ differences with 95% CI; Bonferroni-correct across 3 targets.

for target in [CTSS, BRD3, DNMT1]:
    ligands = load_chembl(target_id, pchembl_min=5)
    train, val, test = scaffold_split(ligands, 0.7, 0.15, 0.15)

    classical_scores = {}
    quantum_scores = {}
    for lig in ligands:
        classical_scores[lig] = vina_dock(lig, target.pdb_structure, seeds=3)
        qubo = formulate_docking_qubo(lig, target.pocket, max_vars=30)
        raw_solution = dwave_sampler.sample_qubo(qubo, num_reads=1000)
        pose = decode_qubo_to_pose(raw_solution.first.sample)
        relaxed_pose = mm_minimize(pose, forcefield='MMFF94')
        quantum_scores[lig] = binding_energy(relaxed_pose)

    features_classical = build_features(train, [classical_scores, rdkit_descriptors])
    features_fused = build_features(train, [classical_scores, quantum_scores, rdkit_descriptors])

    model_classical = XGBRegressor().fit(features_classical, pchembl_train)
    model_fused = XGBRegressor().fit(features_fused, pchembl_train)

    rho_classical = spearman(model_classical.predict(test_features_classical), pchembl_test)
    rho_fused = spearman(model_fused.predict(test_features_fused), pchembl_test)

    bootstrap_ci(rho_fused - rho_classical, n=1000)
    report(target, rho_classical, rho_fused, plausibility_check(quantum_scores))