Quantum-AI Convergence: Redefining the Epistemology of Computational Drug Discovery
Abstract
Classical biomedical simulation such that molecular dynamics, protein folding and in silico screening face significant limitations including scaling exponentially, crude approximations, and long simulation times. Since biological simulations are fundamentally quantum; classical models rely on crude approximations. Instead, quantum simulation brings to qubits a mapping of the systems of molecules, such that it is possible to compute their energetics and conformations with fidelity to physics. A hybrid digital pipeline with quantum techniques consists of uniting artificial intelligence (AI) with massive biomedical data warehousing, quantum-enhanced solvers compare interactions at the atomic scale, and results are reinvested in prioritization before anything is done in a wet lab. This review synthesizes the existing literature and summarizes the existing knowledge in the field of peer-reviewed articles as algorithms (e.g., VQE, annealing, QML), biomedical application (drug discovery, oncology, genomics, diagnostics), and results of the process (accuracy, runtime, cost, and reproducibility). The findings demonstrate an evident shift to hybrid quantum classical architectures that are more fidelity to modeling, less assays early stage due to virtual experimentation, and compress the research-to-treatment timeline claiming that quantum-AI is an emergent experimental basis of biomedicine.
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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