Why combinatorial optimization in drug discovery?
Drug discovery involves searching enormous combinatorial spaces: protein conformations, molecular fragment combinations, clinical trial cohort assignments, and compound library screening strategies. Many of these reduce to binary or integer optimization problems over energy landscapes that are a natural fit for QUBO solvers and quantum-inspired methods.
Protein side-chain packing
Determine the energetically favorable conformation of protein side chains. Each residue selects from a discrete rotamer library — a binary assignment problem that maps to a QUBO over pairwise interaction energies.
Molecular docking (pose selection)
Select the binding pose of a small molecule in a protein active site from a discrete set of candidate conformations. Minimize docking energy subject to steric clash constraints.
Lead compound selection
Select a diverse, property-matched subset of compounds from a virtual library for wet-lab synthesis and screening. Cardinality-constrained, multi-objective optimization.
Clinical trial cohort design
Assign patients to trial arms to balance covariates (age, sex, baseline severity) while meeting regulatory requirements for randomization and block size.
Molecular ground state (VQE)
Compute ground-state energy of small molecules using the Variational Quantum Eigensolver. Directly relevant to understanding binding affinity and reaction pathways.
VQE for molecular simulation
NEROX VQE runs on simulated quantum circuits (up to ~50 qubits) and is designed for molecular Hamiltonians mapped from second-quantized electronic structure. Use it to benchmark quantum-classical hybrid methods on small molecules before migrating to real quantum hardware.
Data security for pharmaceutical research
NEROX on-premise deployment ensures compound structures, binding data, and clinical trial designs never leave your data center. The solver stack is air-gapped deployable and passes standard pharmaceutical IT security audits. Contact us for a GxP compliance assessment.