Decoding Protein Acidity with Quantum-Inspired AI
![The pursuit of accurate residue-level [latex] pK_a [/latex] prediction has evolved through distinct methodological approaches, initially leveraging descriptor-driven resources like DeepKaDB-built upon curated features from soluble proteins-and subsequently expanding with simulation-driven datasets such as PHMD549-which utilizes GPU acceleration to extend PHMD279 to over 26,000 residues-culminating in hybrid quantum-classical frameworks like DQNN that integrate curated descriptors with quantum-inspired feature transformations.](https://arxiv.org/html/2603.11061v1/Threepanelschemati.png)
A new approach combines the power of quantum computing principles with deep learning to significantly improve the prediction of pKa values – critical for understanding protein behavior.
![The pursuit of accurate residue-level [latex] pK_a [/latex] prediction has evolved through distinct methodological approaches, initially leveraging descriptor-driven resources like DeepKaDB-built upon curated features from soluble proteins-and subsequently expanding with simulation-driven datasets such as PHMD549-which utilizes GPU acceleration to extend PHMD279 to over 26,000 residues-culminating in hybrid quantum-classical frameworks like DQNN that integrate curated descriptors with quantum-inspired feature transformations.](https://arxiv.org/html/2603.11061v1/Threepanelschemati.png)
A new approach combines the power of quantum computing principles with deep learning to significantly improve the prediction of pKa values – critical for understanding protein behavior.

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![LLVQ consistently achieves superior performance to standard vector quantization methods-as demonstrated by lower perplexity on wikitext-2 and improved results on both the CSR and MMLU benchmarks-even when considering compression efficiency measured in bits per weight [latex] BPW [/latex].](https://arxiv.org/html/2603.11021v1/x2.png)
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![The asymmetry [latex]A_{UT}^{\sin\phi_s}[/latex] was projected using three distinct models [latex]S_k[/latex] for quark-gluon [latex]qg[/latex] and quark-antiquark [latex]\bar{q}q[/latex] fragmentation, revealing the sensitivity of next-to-leading-order calculations to underlying assumptions about hadronization-a dependency further quantified by scale variations spanning [latex]\mu\in[Q/2,2Q][/latex].](https://arxiv.org/html/2603.10628v1/x9.png)
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