# Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction

arXiv: https://arxiv.org/abs/2603.11061 · HTML: https://mlsanity.com/paper/2603.11061/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks](https://mlsanity.com/paper/2012.06051/) (2020)
- [DPFAGA-Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network](https://mlsanity.com/paper/2503.15563/) (2025)
- [QuXAI: Explainers for Hybrid Quantum Machine Learning Models](https://mlsanity.com/paper/2505.10167/) (2025)
- [Compressed Sensing Based Data Processing and MAC Protocol Design for Smartgrids](https://mlsanity.com/paper/1506.08318/) (2015)
- [SpoofTrackBench: Interpretable AI for Spoof-Aware UAV Tracking and Benchmarking](https://mlsanity.com/paper/2510.22726/) (2025)

## Built on by (most cited first, 1 of 1)

- [TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection](https://mlsanity.com/paper/2606.05779/) (2026)

## Abstract (arXiv)

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 Aβ40 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.