Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction
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 architect...
| Authors | Van Le, Tan Le |
|---|---|
| Primary category | physics.chem-ph |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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