AI Learns to Predict Protein Function from Scratch

A new study from Google DeepMind is pushing the boundaries of how artificial intelligence can understand biology. Scientists have trained a model called AlphaFold3 that does not just predict protein structure like its predecessor, but can now also simulate how proteins interact with DNA, RNA, and small molecules. This brings researchers a step closer to modeling complex molecular machinery inside cells.

The innovation lies in how AlphaFold3 combines physical principles with deep learning, using a single unified architecture to simulate biomolecular complexes. Instead of relying on existing templates, the model generates predictions from raw sequence data. This means it can handle previously unseen combinations of biological components, giving scientists the ability to explore novel interactions that were hard or impossible to study before.

The model has already demonstrated major improvements in accuracy over existing tools. It predicts not just the shapes of individual proteins but also how they bind to other molecules, which is essential for understanding how cells work and how diseases begin. This is also critical for designing new drugs that target specific biological pathways.

While the full version of AlphaFold3 is not yet publicly released, DeepMind has launched a service that allows researchers to use the tool for non commercial purposes. This opens the door to new research in structural biology, drug discovery, and synthetic biology, where understanding how biological parts fit and move together is key.

By giving scientists access to predictions about protein interactions on a scale never before possible, this new AI model could accelerate discoveries across medicine and biotechnology.

https://www.deepmind.com/blog/alphafold-3-a-unified-model-of-the-protein-universe