Daiichi Sankyo Uses QSimulate's QUELO Quantum Chemistry Software in Several Drug Discovery Programs to Predict Binding Affinity: What the June 2026 AWS Account Shows
How Daiichi Sankyo uses QSimulate's quantum chemistry to predict drug binding
Quentir Medicine Monitor
Evidence-based insights for quantum medicine.
Daiichi Sankyo is using software from the Boston start-up QSimulate to bring quantum chemistry into one of the most routine and most expensive judgments in drug discovery: how tightly a candidate molecule will bind to its protein target. The main public account is a Japanese-language blog on the Amazon Web Services site, published on 11 June 2026.
That blog was written jointly by scientists from Daiichi Sankyo's Smart Research Institute II, by QSimulate, and by an AWS solutions architect, and it describes how the company evaluated and then adopted QSimulate's QM-FEP method, a version of free energy perturbation that uses quantum mechanics for the energy of the interaction between drug and protein.
Predicting binding affinity well matters because it decides which molecules a medicinal chemistry team actually makes. Each round of the design, make, test and analyze cycle costs weeks of synthesis and assay work. A computer model that ranks candidates more reliably lets chemists spend that time on the compounds most likely to work, and lets them drop weak ideas before anyone orders reagents.
No quantum computer is involved here. QSimulate's software runs quantum mechanical calculations on ordinary graphics processors in the cloud. The interest for medicine lies in a large pharmaceutical company putting physics-level chemistry into its everyday design work today, years before quantum hardware is expected to take on the same problem.
The Daiichi Sankyo authors explain the problem in practical terms. Their teams already use structure-based drug design and conventional free energy perturbation, which estimates binding by simulating the gradual transformation of one molecule into another inside the protein. That method depends on a classical force field, a fixed set of rules for how atoms push and pull on each other. The authors write that more accurate energy functions are needed, especially for the chemical modalities they expect to grow more complex, where conventional FEP has struggled with non-classical interactions and with covalent drugs that form a chemical bond with their target.