aceQuantum-Enhanced Transfer Learning for IDR Binding Partner Prediction
Intrinsically Disordered Regions (IDRs) play essential roles in cellular processes through interactions with proteins, nucleic acids, lipids, and metal ions, yet predicting their binding partners remains challenging for understanding protein function and drug discovery. However, current computational methods including protein language models face performance plateaus where traditional approaches to improve accuracy have become ineffective. Here, we present a hybrid quantum-classical machine learning approach tha…
A hybrid quantum-classical model combining variational quantum circuits with ESM2 achieved statistically significant improvements over classical baselines for multi-class IDR binding partner prediction.
Evidence
- Peer-reviewedIEEE Transactions on Computational Biology and Bioinformatics2026-08-17
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Truvace Impact Record TRV-2026-0819, v1: “Quantum-Enhanced Transfer Learning for IDR Binding Partner Prediction.” Truvace, 2026-08-18. /record/TRV-2026-0819 (accessed at citation time). sha256 044bff0c4b26a5cb…
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