Google DeepMind has quietly open-sourced its frontier artificial intelligence (AI) model that can predict interactions between proteins and other molecules. Named AlphaFold 3, the large language model is the successor to AlphaFold 2, whose research led to the large language model (LLM) creators Demis Hassabis and John Jumper receiving the Nobel Prize in Chemistry in 2024. AlphaFold 3 takes capabilities further Its ability to model the interactions of proteins with DNA, RNA and other small molecules could potentially lead to drug discovery.
Google DeepMind open-sourced AlphaFold 3 AI model
Research on protein structures has been one of the major areas of focus in chemistry. Since the 3D shape and atomic details of proteins are targets for drugs, discovery of new protein structures can often open up previously unknown targets and mechanisms for medical intervention. Simply put, the better we understand protein structures, the more effective drugs can be against various disorders, diseases and autoimmune disorders.
While Google DeepMind has not made any announcements about releasing the AlphaFold 3 AI model, it has made the source code and model weights available on GitHub. However, it is available for academic and research purposes only. The source code is available for free under a Creative Commons license, however, Vet can only be accessed after obtaining permission directly from Google for academic use.
It is believed that if AI models can accurately uncover how proteins interact with DNA, RNA and other small molecules, researchers will be able to accelerate the creation of new synthetic drugs.
Researchers will also be able to automate work that might have taken them years without any evidence of success. The AlphaFold 3 arrives three years after the release of the AlphaFold 2 in 2021. In one study, the lead author highlighted that drug discovery can be much easier with the help of AI models.
AlphaFold 3 has been trained on a large amount of research material and datasets about protein structures and their interactions with other molecules. By understanding the context and logic of protein structures, LLM can predict how certain target regions will react when exposed to certain molecules.
