AlphaFold 3 brings DeepMind AI to DNA, RNA and drugs

Google DeepMind and Isomorphic Labs unveiled AlphaFold 3 this Wednesday, a new version of their artificial intelligence system for predicting molecular structures. Its biggest breakthrough is that it is no longer limited to proteins: it can model how they interact with DNA, RNA, small molecules, ions and chemical modifications.

That matters because much of biology and drug development depends precisely on these interactions. An isolated protein can provide useful information, but many diseases and treatments are explained by the way a protein binds to another molecule inside a cell.

From folding proteins to reconstructing biological complexes

AlphaFold 2, unveiled in 2020, solved one of the major problems in computational biology: predicting a protein’s three-dimensional shape from its amino acid sequence. That shape determines its function. The system transformed the work of many laboratories by providing structural hypotheses in seconds or minutes—something that previously could require years of experiments.

AlphaFold 3 expands that approach. Rather than estimating only a protein’s geometry, it attempts to generate the combined structure of a molecular complex—for example, a protein bound to a fragment of DNA, an RNA molecule or a chemical compound.

Small molecules are especially relevant to the pharmaceutical industry. A drug typically works by binding to a specific region of a protein and altering its activity. Molecules that bind to a protein are known as ligands. Predicting their position and orientation could help researchers identify earlier which candidates are worth synthesizing and testing in the lab.

According to Google DeepMind, AlphaFold 3 is 50% more accurate than the best existing methods for predicting interactions between proteins and other classes of molecules. In some categories of molecular interaction, the company says it doubles the accuracy available to date.

A diffusion model for positioning atoms

The new version introduces a diffusion model, a family of techniques also used to generate images. The mechanism starts with an imprecise molecular arrangement and progressively refines it until it produces coordinates for the atoms in the complex.

This is not a direct photograph of a cell or a complete simulation of everything that happens inside it. AlphaFold 3 produces a prediction based on patterns learned from known structures and provides confidence scores so researchers can distinguish the most reliable regions from the more uncertain ones.

The advance is notable because protein structures involving DNA, RNA or ligands are harder to determine than isolated proteins. Experimental methods such as X-ray crystallography, nuclear magnetic resonance and cryo-electron microscopy remain essential, but they are not always fast, inexpensive or applicable to every molecule.

A tool with direct potential for drug development

Isomorphic Labs, an Alphabet company focused on drug discovery, took part in developing AlphaFold 3. That collaboration makes the technology’s practical goal clear: reducing the number of experiments needed to find promising molecules.

Its potential uses extend beyond drugs. Predicting protein–DNA complexes could help researchers study gene regulation; interactions with RNA are relevant to investigating cellular mechanisms and RNA-based therapies; and the ability to incorporate ions and chemical modifications brings the model closer to more realistic biological systems.

But a plausible structure does not automatically turn a compound into a drug. AlphaFold 3 cannot determine on its own whether a molecule will reach the right tissue, be safe, cause toxicity or work in patients. Nor does it replace laboratory or clinical trials. Its value lies in narrowing down an enormous space of possibilities before those slow and costly stages.

Free access, but not open source

Google DeepMind has made AlphaFold Server available to researchers for noncommercial use. The platform allows users to enter combinations of proteins, DNA, RNA and small molecules without having to install the model or maintain specialized computing infrastructure.

The company has not released AlphaFold 3’s weights or code as open-source software. Server access makes it easier for more academic groups to use the system, but limits their ability to reproduce, adapt or run it independently. That is an important distinction from the practice followed in parts of scientific research, especially when a tool can influence the discovery of treatments.

AlphaFold 3’s main role will be as a tool for asking better experimental questions. If its predictions are consistently confirmed in the lab, AI will have moved from describing isolated proteins to playing a role in a much broader phase of biomedical research.