Screening and Generation of Molecules as Drug Candidates

Discovering and selecting promising drug candidates from large chemical libraries is slow, costly, and often limited by data and computational power.

Quantum machine learning methods such as QGANs, QSVCs, and quantum graph algorithms are being applied to generate new molecules, screen large datasets, and improve predictions of drug safety and behaviour.

These tools can speed up the drug discovery pipeline, improve hit rates, and help identify safer and more effective drug candidates.