Five years ago, a groundbreaking AI system called AlphaFold 2 solved one of biology’s most enduring challenges: predicting the 3D structure of proteins from their amino acid sequences. This achievement, which earned a Nobel Prize in Chemistry in 2024, has since accelerated scientific discovery on an unprecedented scale. Proteins are the microscopic machines that drive every process in living cells, and understanding their folded shapes is crucial for drug development and disease research. When proteins misfold, it can lead to conditions like Alzheimer’s and Parkinson’s—making AlphaFold’s capabilities transformative for medicine.
The impact became truly global when the AlphaFold Protein Database launched in partnership with EMBL-EBI. This freely available resource now contains predictions for over 200 million protein structures—work that would have taken hundreds of millions of years to complete experimentally. More than 3 million researchers across 190 countries have used the database, with over 30% of related research focusing on disease understanding and human welfare.
Real-world applications demonstrate AlphaFold’s practical value. In conservation science, researchers have used it to understand immunity proteins in honeybees, supporting breeding programs for healthier pollinators. Meanwhile, in cardiovascular medicine, AlphaFold revealed the complex structure of apolipoprotein B100—the central protein in “bad cholesterol”—providing pharmaceutical researchers with the blueprint needed to design new heart disease therapies.
Beyond accelerating established research, AlphaFold has democratized structural biology. Turkish undergraduate students taught themselves the field using online tutorials and have since published 15 research papers. The technology has been cited in more than 35,000 papers, with users reporting over 40% increases in novel protein structure submissions. These structures often explore previously uncharted biological territory, with AlphaFold-linked research being twice as likely to be cited in clinical articles.
The evolution continues with AlphaFold 3, which predicts interactions between all life’s molecules—not just proteins, but DNA, RNA, and drug compounds. This holistic view enables researchers to see how potential medicines bind to their targets or how proteins interact with genetic material. The AlphaFold Server has already helped make more than 8 million structural predictions for thousands of researchers worldwide.
Inspired by AlphaFold’s success, new AI models are emerging to tackle broader biological challenges. AlphaMissense and AlphaGenome assess disease-causing genetic mutations, while AlphaProteo designs novel protein binders targeting molecules associated with cancer and diabetes. These developments signal a new era of “digital biology,” where AI accelerates scientific discovery across multiple frontiers—from fusion energy to Earth sciences—empowering researchers to address humanity’s most pressing challenges.