# AlphaFold3 Released as Open Source for Public Use: A New Era in Protein Folding Research
In a groundbreaking development for the scientific community, **AlphaFold3**, the latest iteration of the revolutionary protein structure prediction tool, has been released as open-source software for public use. This move marks a significant milestone in the democratization of cutting-edge scientific tools, enabling researchers, educators, and developers worldwide to access and contribute to one of the most advanced systems for predicting protein structures. The release of AlphaFold3 is expected to accelerate research in fields ranging from drug discovery to synthetic biology, and even to the understanding of fundamental biological processes.
## What is AlphaFold?
AlphaFold is an artificial intelligence (AI) system developed by **DeepMind**, a subsidiary of Alphabet, that predicts the 3D structure of proteins from their amino acid sequences. Proteins are the molecular machines of life, and their function is largely determined by their structure. Understanding how proteins fold into their functional forms has been one of the most challenging problems in biology for decades, often referred to as the “protein folding problem.”
The first version of AlphaFold made headlines in 2018 when it won the **Critical Assessment of Structure Prediction (CASP13)** competition, a biennial event that evaluates the accuracy of protein structure prediction methods. However, it was **AlphaFold2**, released in 2020, that truly revolutionized the field. AlphaFold2 achieved near-experimental accuracy in predicting protein structures, a feat that was previously thought to be decades away. The system was hailed as one of the most significant breakthroughs in biology in recent years.
## AlphaFold3: What’s New?
While AlphaFold2 was a monumental leap forward, AlphaFold3 builds on its predecessor with several key improvements:
1. **Increased Accuracy and Speed**: AlphaFold3 incorporates more advanced machine learning algorithms and optimizations, allowing it to predict protein structures with even greater accuracy and at a faster rate. This is particularly important for large-scale projects, such as mapping the entire proteome of an organism.
2. **Improved Handling of Complexes**: One of the limitations of AlphaFold2 was its difficulty in predicting the structures of protein complexes—groups of proteins that interact with each other to perform biological functions. AlphaFold3 addresses this limitation by offering enhanced capabilities for predicting multi-protein interactions, which is crucial for understanding cellular processes and designing new drugs.
3. **User-Friendly Interface**: AlphaFold3 comes with a more intuitive and accessible interface, making it easier for non-experts to use the tool. This is expected to broaden its adoption in educational settings and among smaller research labs that may not have the computational resources or expertise to run complex AI models.
4. **Open-Source and Extensible**: Perhaps the most significant change is that AlphaFold3 has been released as open-source software. This means that anyone can access, modify, and improve the code. The open-source nature of AlphaFold
- Source Link: https://platohealth.ai/alphafold3-goes-open-source/