Every drug on a pharmacy shelf took 10–15 years and over $2.5B to develop — much of it spent estimating how tightly a molecule binds its protein target.
For most drugs, classical methods handle this well. But for metalloenzymes — kinases, CYP450 enzymes, the targets behind cancer, drug metabolism and antibiotic resistance — force fields miss the quantum nature of metal-ligand bonds, pushing binding-energy errors well past the ~1 kcal/mol needed for reliable predictions.
𝗧𝗵𝗿𝗲𝗲 𝗺𝗲𝘁𝗵𝗼𝗱𝘀, 𝗼𝗻𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸:
🔹 𝗠𝗠/𝗙𝗘𝗣 — fast, industry-standard, but blind to quantum metal-ligand bonding.
🔹 𝗗𝗙𝗧 (𝗤𝗠/𝗠𝗠) — better, but fails on strongly correlated electrons.
🔹 𝗩𝗤𝗘 — simulates the active site directly on quantum hardware, no DFT approximation needed.
𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: 𝗕𝗼𝗲𝗵𝗿𝗶𝗻𝗴𝗲𝗿 𝗜𝗻𝗴𝗲𝗹𝗵𝗲𝗶𝗺 × 𝗚𝗼𝗼𝗴𝗹𝗲 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗔𝗜
Since 2021, this partnership has run VQE on Google’s Sycamore processor to simulate drug fragments and metal-coordination complexes. On small test molecules, VQE energies already match classical CCSD(T) — real drug targets are still out of reach for now, but it’s the same VQE approach used in our Novel Materials Design work, just applied to a new domain.
𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗘𝘂𝗿𝗼𝗽𝗲
Quantum simulation cuts across pharma, materials and chemistry — all EU Quantum Flagship priorities. At Quantum+, we track how the same algorithms move between these domains, from lab to industry.
𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘄𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘀𝗽𝗲𝗲𝗱 𝘂𝗽 𝗱𝗿𝘂𝗴 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆.
𝗜𝘁 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗲𝗮𝗹 𝗺𝗲𝗱𝗶𝗰𝗶𝗻𝗲𝘀 𝗰𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀 𝗰𝗼𝘂𝗹𝗱 𝗻𝗲𝘃𝗲𝗿 𝗳𝗶𝗻𝗱.