Quantum-Circuit Residuals for Receptor Functional Potency Prediction: Predictive Gains and Quadratic Structure
The predictive performance of neural networks for receptor functional potency modeling depends on both molecular representations and prediction-head design. Using GLP-1R cAMP-related pEC50 prediction as the primary task, we jointly train a classical molecular encoder with a linear base head and a shallow quantum-circuit residual head that share the learned molecular representation. We evaluate the models using random and structure-group splits, include tuned Chemprop as a domain reference, and extend the evaluation to ADRB2 and MC4R. Under the GLP-1R structure-group split, the quantum-residual models reduce mean absolute error relative to the corresponding base models from 0.677 to 0.639 with a multilayer perceptron (MLP) encoder and from 0.680 to 0.641 with a graph neural network (GNN) encoder. In structural control experiments, a parameter-count-matched real-orthogonal control achieves similar predictive performance, suggesting that some quantum-residual gains may be associated with structured quadratic feature interactions. By combining receptor functional potency prediction with structural analysis of the quantum residual, this study provides empirical evidence to inform compact residual-head design and highlights the potential of quantum-circuit parameterizations for this task.