Quantum Machine Learning for Drug–Target Interaction Prediction: A Hybrid Variational Circuit Approach on NISQ Hardware

Large compound libraries are reduced to candidate’s worth testing by computational prediction of drug-target interactions (DTI). We investigate whether this effort is aided by variational quantum circuits appropriate for noisy intermediate-scale quantum (NISQ) devices. Our hybrid model uses classical networks to encode ECFP4 fingerprints and protein sequence descriptors, compresses them to eight rotation angles, and then processes them using a four-layer hardware-efficient circuit that is read out using local Pauli-Z expectations. Realistic noise models are used to simulate the circuits, and it is trained end-to-end using gradients that match the parameter-shift algorithm. The model achieved test AUROCs of 0.896, 0.892, 0.911, and 0.878 on Davis, KIBA, BindingDB, and BIOSNAP. These results outperform a kernel SVM and logistic regression, although they lag behind a graph network on three datasets, a deep network (0.915–0.927), and gradient-boosted trees. Removing entanglement had little effect on the performance of a parameter-matched classical head, and no low-data advantage emerged. Hardware tolerance was demonstrated by the circuit itself. Because ranking is invariant to uniform noise attenuation, local readout avoided barren plateaus, 256 shots were sufficient, and 1% two-qubit error cost only 0.0025 AUROC. Calibration was recovered by recalibrating the reading. Hardware training costs, not noise, are the binding restriction. For drug-screening decisions, these predictions provide an evidence-based ranking of candidate compound–target pairs for experimental follow-up. The comparison with classical methods indicates whether adding a quantum circuit is justified by the resulting predictive performance and computational cost.