Quantum Attention GANs for Enhanced Compound– Protein Interaction Prediction
Bibliographic record
Abstract
The prediction of compound–protein interactions (CPIs) is a vital task in computational biology, driving advancements in drug discovery, protein function analysis, and understanding of complex biological processes. However, existing models face challenges in effectively capturing quantum-level molecular interactions, particularly when representing DNA–protein complexes within quantum computational frameworks. To overcome these limitations, we introduce a novel Hamiltonian Multi-Quantum Head Generative Self Dandelion Adversarial Attention Network (HMQ-HGS-D2AN). The proposed approach leverages molecular qubit representation, encoding each base pair or amino acid as a qubit state while mapping structural parameters to Bloch sphere coordinates, thereby enriching the quantum characterization of biomolecular interactions. The methodology incorporates fuzzy min–max neural network– based preprocessing, feature mapping through the Discrete Cosine–Krawtchouk–Tchebichef Transform (DCKTKT), and dimensionality reduction using the Prairie Dog Optimization Algorithm. At its core, the architecture integrates Hamiltonian Quantum Generative Adversarial Networks (HQuGANs) with multi-head self-attention, optimized via the Dandelion Optimizer Algorithm (DOA), producing robust and discriminative quantum state representations. Experimental validation on human and C. elegans datasets highlights the model’s effectiveness, achieving an exceptional 99.9% accuracy in CPI prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".