Graphics processing unit-accelerated quantum simulation for molecular biology and drug discovery: algorithms, platforms, and emerging applications

Graphics processing unit-accelerated quantum simulation for molecular biology and drug discovery: algorithms, platforms, and emerging applications. Artificial intelligence has intensified the need for methods that describe molecular systems with quantum-mechanical accuracy. Gate-based quantum computing offers a principled framework for simulating electronic structure, molecular interactions, and biological processes at a fidelity inaccessible to classical force fields or density functional theory alone, yet scarce, noisy hardware limits direct experimentation. Graphics processing unit (GPU)-accelerated quantum simulation has emerged as a practical bridge, allowing quantum algorithms to be developed, validated, and benchmarked on classical infrastructure at 30-40 noiseless qubits, and far beyond where tensor-network methods apply. Unlike recent reviews organized around quantum machine learning (QML) or around hardware, this review is organized around the simulation layer itself and asks, for each domain, what can be established in simulation, what requires a quantum processor, and how the two connect in one hardware-agnostic workflow. We examine how the variational quantum eigensolver, quantum phase estimation, and sample-based hybrid schemes address electronic structure in small-molecule pharmacology; how quantum-enhanced conformational sampling complements AlphaFold; and how QML introduces alternative feature spaces for drug-target, structure-activity, and genomic modeling. We compile published GPU benchmarks, identify the molecular systems for which tensor-network simulation is and is not adequate, explain how GPU platforms accelerate error mitigation, and critically assess 2023-6 developments, including quantum-centric supercomputing, hybrid quantum Monte Carlo on trapped-ion hardware, protein binding-site prediction on utility-scale processors, below-threshold error correction, and a classically verifiable quantum-advantage claim. Framing GPU simulation as a nea…