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Replies to post #251733 on Biotech Values
Artificial intelligence can now be used to predict how all of life’s molecules interact with each other with “unprecedented accuracy”, scientists have said.
The program, called AlphaFold 3, could help supercharge the hunt for new drugs and treatments for some of humanity’s most devastating diseases, such as cancer, Parkinson’s, malaria, tuberculosis and many more, according to its creators Google DeepMind.
AlphaFold 3 is able to envision how the complex shapes and networks of molecules – present in every cell in the human body – are connected and how the smallest of changes in these can affect biological functions that can lead to diseases.
It can also help scientists predict how these molecules will interact with potential treatments, such as antibodies and drugs.
Sir Demis Hassabis, founder and chief executive of London-based DeepMind, said the program gives researchers a “toolset”, that can “increase the speed of the drug discovery process massively” and “transform our understanding of the Biological world.”
In this paper, we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture, which is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues. The new AlphaFold model demonstrates significantly improved accuracy over many previous specialised tools: far greater accuracy on protein-ligand interactions than state of the art docking tools, much higher accuracy on protein-nucleic acid interactions than nucleic-acid-specific predictors, and significantly higher antibody-antigen prediction accuracy than AlphaFold-Multimer v2.37,8. Together these results show that high accuracy modelling across biomolecular space is possible within a single unified deep learning framework.
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