A protein conformational change is often described as a transition from one structural state to another: open to closed, inactive to active, or apo to ligand-bound. That language is useful for describing structural differences, but it can obscure the physical problem that determines how a protein behaves in solution.

Artificial intelligence has changed the starting point for structural biology. Protein structure prediction tools such as AlphaFold can generate highly accurate structural models for many proteins, including proteins for which experimentally determined structures are unavailable.

Structure-based drug design uses the three-dimensional structure of a target protein to identify functional binding sites and design drug molecules that complement their shape, size, and electrostatic properties. This approach has enabled major advances in medicine, including HIV protease inhibitors, antibiotics, and targeted cancer therapies such as imatinib.