Quantum-Inspired Hamiltonian Feature Extraction for ADMET Prediction: A Simulation Study
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912940112740352 |
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| author | Benson, B. Maurice Byler, Kendall Petroff, Anna Keinan, Shahar Shipman, William J |
| author_facet | Benson, B. Maurice Byler, Kendall Petroff, Anna Keinan, Shahar Shipman, William J |
| contents | Predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties remains a critical bottleneck in drug discovery. While molecular fingerprints effectively capture local structural features, they struggle to represent higher-order correlations among molecular substructures. We present a quantum-inspired feature extraction method that encodes molecular fingerprints into a parameterized Hamiltonian, using mutual information (MI) to guide entanglement structure. By simulating quantum evolution on GPU-accelerated backends, we extract expectation values that capture pairwise and triadic correlations among fingerprint bits. On ten Therapeutic Data Commons (TDC) ADMET benchmarks, our method achieves state-of-the-art performance on CYP3A4 substrate prediction (AUROC 0.673 0.004) and improves over classical baselines on 8/10 tasks. SHAP (SHapley Additive exPlanations) analysis reveals that quantum-derived features contribute up to 33% of model importance despite comprising only 1.6% of features, demonstrating that Hamiltonian encoding concentrates predictive signal. This simulation study establishes the foundation for hardware validation on near-term quantum devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03109 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum-Inspired Hamiltonian Feature Extraction for ADMET Prediction: A Simulation Study Benson, B. Maurice Byler, Kendall Petroff, Anna Keinan, Shahar Shipman, William J Quantum Physics Predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties remains a critical bottleneck in drug discovery. While molecular fingerprints effectively capture local structural features, they struggle to represent higher-order correlations among molecular substructures. We present a quantum-inspired feature extraction method that encodes molecular fingerprints into a parameterized Hamiltonian, using mutual information (MI) to guide entanglement structure. By simulating quantum evolution on GPU-accelerated backends, we extract expectation values that capture pairwise and triadic correlations among fingerprint bits. On ten Therapeutic Data Commons (TDC) ADMET benchmarks, our method achieves state-of-the-art performance on CYP3A4 substrate prediction (AUROC 0.673 0.004) and improves over classical baselines on 8/10 tasks. SHAP (SHapley Additive exPlanations) analysis reveals that quantum-derived features contribute up to 33% of model importance despite comprising only 1.6% of features, demonstrating that Hamiltonian encoding concentrates predictive signal. This simulation study establishes the foundation for hardware validation on near-term quantum devices. |
| title | Quantum-Inspired Hamiltonian Feature Extraction for ADMET Prediction: A Simulation Study |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2603.03109 |