Protein Structure in Drug Design — From Target to Treatment

Why structure matters for drugs

Most drugs work by binding to proteins — fitting into specific pockets on the protein's surface and either blocking or modifying its function. To design effective drugs, you need to know the 3D shape of the target protein and its binding sites. This is structure-based drug design (SBDD), and it has been responsible for some of the most successful drugs in history, including HIV protease inhibitors and many cancer therapies.

The drug design pipeline

The traditional pipeline starts with identifying a drug target (usually a protein involved in disease), determining its 3D structure (by X-ray crystallography, cryo-EM, or NMR), identifying binding pockets, and then designing or screening molecules that fit those pockets. This is followed by lead optimization (improving the binding affinity and drug-like properties), preclinical testing, and clinical trials.

Virtual screening

Once you have a protein structure, you can screen millions of potential drug molecules computationally using molecular docking. Each molecule is "docked" into the protein's binding site, and a scoring function estimates how well it binds. The top-scoring molecules are then tested experimentally. This approach is orders of magnitude faster and cheaper than experimental high-throughput screening.

How AlphaFold is changing the game

Before AlphaFold, structure-based drug design was limited by the availability of experimental structures. Only about 170,000 experimental structures existed in the Protein Data Bank. AlphaFold has expanded this to over 200 million predicted structures — meaning that virtually any drug target now has a structural model available.

Pharmaceutical companies are already using AlphaFold structures for drug discovery programs. The key caveat is that AlphaFold predictions are static — they show one conformation — and drug binding often involves conformational changes. Still, AlphaFold structures provide an excellent starting point for computational drug design.

Explore the structure of any potential drug target using our structure predictor.