Wang Zha open source! Google open sources the Nobel Prize-winning chemistry model Alphafold-3, changing the world overnight!

This morning, Google finally open-sourced the highly anticipated protein prediction model—AlphaFold-3!

Last month, Google DeepMind co-founder and CEO Demis Hassabis won the Nobel Prize in Chemistry, thanks to AlphaFold-3, which shows the model's significant value.

The world's top scientific journal, Nature, also made a heavy recommendation, which will have a major impact on the global research field. Scientists in biology, chemistry, and medicine can deploy AlphaFold-3 locally, significantly shortening the development process for new drugs, vaccines, and more.

It is said that AI benefits all of humanity, and the fields of medicine and research are among the most important application scenarios, which is why AlphaFold-3 was able to win the Nobel Prize.

Netizens couldn't help but curse when they saw this model was open-sourced!

AlphaFold-3 being open-sourced is just too amazing.

Awesome. Can't wait to see higher-level biological simulations.

Google's great initiative. Can't wait to see what impact this will have on life sciences.

Incredible news! The open-sourcing of AlphaFold-3 could become a game-changer for scientific discovery. Can't wait to see the breakthroughs it inspires in protein research!

It's fantastic that Google can share this! It really can save lives.

Google's open-source AlphaFold-3 model is very low-key this time, and there hasn't even been a press release; only a small note was added to the original article:

Updated on November 11, 2024, we have released the model code and weights for AlphaFold 3 for academic use to help advance scientific research.

The main features of AlphaFold-3 include the ability to predict the structures of various biomolecules, covering almost all types of molecules present in the Protein Data Bank (PDB), including proteins, nucleic acids (including DNA and RNA), small molecules, ions, and modified residues.

In protein structure prediction, AlphaFold 3 demonstrates extremely high accuracy, being able to accurately determine the three-dimensional folding of proteins when predicting monomer structures, including the precise locations and orientations of secondary structures such as α-helices and β-sheets, as well as the relative positions and orientations of various domains in the tertiary structure.

AlphaFold 3 excels in handling complex structures, capable of addressing biomolecular complexes composed of numerous residues and various molecular components. For example, when predicting the structure of protein-nucleic acid complexes composed of thousands of residues, it can effectively integrate information from both protein and nucleic acid molecules to accurately construct a three-dimensional structural model of the entire complex.

For complexes with high symmetry or repetitive structural units, such as nucleosomes, AlphaFold 3 can also accurately capture their structural features, including the interactions between histones and DNA, and the arrangement of nucleosomes.

Source: AIGC Open Community, original title: "Big Bang Open Source! Google Open Sources Nobel Prize Chemistry Model AlphaFold-3, Changing the World Overnight!"