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AI Designs Better Drug Candidates with Quantum Aid

Researchers from Japan Tobacco and D‑Wave have unveiled a hybrid framework that merges deep generative models with quantum annealing to design drug‑like molecules. The system introduces a Neural Hash Function that enables binarisation for quantum processing while preserving gradient flow. Generated compounds show higher validity and drug‑likeness than those from purely classical models, even surpassing the training set metrics without explicit optimisation. This demonstrates a scalable way to explore the vast chemical space using quantum‑assisted sampling.