Quantum-Inspired Hamiltonian Feature Extraction for ADMET Prediction: A Simulation Study

• URL: http://arxiv.org/abs/2603.03109v1
• Date: Tue, 03 Mar 2026 15:42:33 GMT
• Title: Quantum-Inspired Hamiltonian Feature Extraction for ADMET Prediction: A Simulation Study
• Authors: B. Maurice Benson, Kendall Byler, Anna Petroff, Shahar Keinan, William J Shipman,
• Abstract summary: We present a quantum-inspired feature extraction method that encodes molecular fingerprints into a parameterized Hamiltonian.<n>On ten Therapeutic Data Commons (TDC) ADMET benchmarks, our method achieves state-of-the-art performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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