Google DeepMind has unveiled Alphafold 3, an advanced update to their groundbreaking AI tool designed to explore the complexities of biological life. This latest version extends beyond protein structures to include DNA, RNA, and other crucial molecules like ligands. According to DeepMind, Alphafold 3 offers an unprecedented, detailed view of molecular interactions, setting a new standard in the field. The tool’s applications are diverse, ranging from enhancing crop resilience to pioneering new vaccine development.

DeepMind’s high expectations for Alphafold stem from its remarkable success in 2020 when the AI tool clinched a victory in an international competition by accurately predicting the structures of 70 out of 100 protein sequences. 

This achievement demonstrated the tool’s potential to extend its capabilities beyond proteins. DeepMind CEO Demis Hassabis highlighted the significance of this advancement in a conference call, stating, “Biology is a dynamic system. The properties of biology emerge from the interactions among various molecules within a cell. Alphafold 3 represents our initial significant step towards modeling these complex properties.”

Alphafold 2 had previously made significant strides in various areas, such as improving the mapping of the human heart, modeling antibiotic resistance, and identifying the eggs of extinct birds. The latest iteration promises even greater potential, particularly in the realm of drug discovery.

Mohammed AlQuraishi, an assistant professor of systems biology at Columbia University who is not associated with DeepMind, is optimistic about the new version’s capabilities. He explains, “The previous Alphafold system was limited to amino acids, which restricted its use in biopharmaceuticals. However, the upgraded system is now theoretically capable of predicting drug binding sites on proteins.”

DeepMind’s subsidiary, Isomorphic Labs, is already applying this model in collaboration with pharmaceutical companies to innovate new treatments for diseases.

AlQuraishi acknowledges that the new version of Alphafold marks a significant advancement. He notes its enhanced generality, especially for early-stage drug discovery, where it now surpasses its predecessor, Alphafold 2. However, he also points out that the effectiveness of Alphafold depends largely on the accuracy of its predictions. For certain tasks, Alphafold 3 achieves a success rate that is double that of other leading models, such as Rosettafold. Yet, for more complex interactions, like those between proteins and RNA, its accuracy remains quite limited.

DeepMind reports that the model’s accuracy can vary significantly, ranging from 40 percent to over 80 percent, depending on the specific molecular interaction being modeled. Moreover, Alphafold informs researchers of the confidence level in its predictions, which helps determine the subsequent steps. For less precise predictions, researchers are advised to use Alphafold as a preliminary tool, supplementing it with other techniques as necessary. Despite the variability in accuracy, for scientists looking to explore initial hypotheses — such as identifying enzymes that could degrade plastics in water bottles — using Alphafold is considerably more efficient than traditional experimental methods like X-ray crystallography.

To handle the increased complexity and the expanded molecule library of Alphafold 3, DeepMind enhanced the underlying model architecture. The team adopted diffusion techniques, which have seen significant advancements by AI researchers over recent years. These techniques are now utilized in powerful image and video generators like OpenAI’s Dall-E 3 and Sora. The process involves training a model to refine a noisy initial image progressively until it achieves an accurate prediction. This method enables Alphafold 3 to manage a much larger set of inputs effectively.

John Jumper, director at Google DeepMind, emphasized the significance of these improvements, stating, “This was a big development compared to the previous model. It really simplified the whole process of all these different atoms working together.”

However, the use of diffusion techniques in Alphafold 3 introduces certain risks. As noted in the Alphafold 3 publication, these techniques enabled the model to ‘hallucinate’ or generate structures that appear feasible but are, in fact, nonviable. The researchers attempted to mitigate this issue by incorporating additional training data in areas most susceptible to such errors, although this solution has not completely resolved the problem.

The potential impact of Alphafold 3 on drug development will also partly depend on how DeepMind manages access to the model. With Alphafold 2, the company made the source code open, allowing researchers to examine it closely and understand its functionality better. This version was freely available for all types of uses, including commercial applications by pharmaceutical companies.

For Alphafold 3, however, Demis Hassabis has indicated that there are no current plans to release the complete code. Instead, DeepMind is providing access through a public interface known as the Alphafold Server, which restricts the range of molecules that can be studied and is limited to non-commercial use. This approach aims to simplify the technology for biologists who may not be as versed in such advanced tools.

According to AlQuraishi, these new limitations are significant. He expresses concern that the model’s primary feature — predicting interactions between proteins and small molecules — is effectively inaccessible to the broader public, describing the current offering as “mostly a lure.”