Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction
This work addresses the challenge of predicting pKa values for protein residues, which is essential for understanding protein function and stability, but it is incremental as it builds on existing classical methods with quantum-inspired enhancements.
The authors tackled the problem of accurate residue-level pKa prediction for proteins by introducing a hybrid quantum-classical encoding framework, which achieved improved cross-context generalization and robustness in benchmarks like PKAD-R and an Aβ40 case study.
Accurate prediction of residue-level pKa values is essential for understanding protein function, stability, and reactivity. While existing resources such as DeepKaDB and CpHMD-derived datasets provide valuable training data, their descriptors remain primarily classical and often struggle to generalize across diverse biochemical environments. We introduce a reproducible hybrid quantum-classical framework that enriches residue-level representations with a Gaussian kernel-based quantum-inspired feature mapping. These quantum-enhanced descriptors are combined with normalized structural features to form a unified hybrid encoding processed by a Deep Quantum Neural Network (DQNN). This architecture captures nonlinear relationships in residue microenvironments that are not accessible to classical models. Benchmarking across multiple curated descriptor sets demonstrates that the DQNN achieves improved cross-context generalization relative to classical baselines. External evaluation on the PKAD-R experimental benchmark and an A$β$40 case study further highlights the robustness and transferability of the quantum-inspired representation. By integrating quantum-inspired feature transformations with classical biochemical descriptors, this work establishes a scalable and experimentally transferable approach for residue-level pKa prediction and broader applications in protein electrostatics.