AlphaFold 3 represents a significant breakthrough in understanding biomolecular structure, allowing for experimental-level accuracy in predicting protein structures and their interactions with small molecules, DNA, and RNA.

AlphaFold 3 is a significant step towards solving the structure prediction problem, but we still aim for 100% accuracy in understanding molecular interactions.

AlphaFold 3 enables drug designers to model not just protein structures but also how these proteins interact with small molecules, enhancing our ability to design effective drugs.

The first generation of AI applications in drug design often focused on local models, but our approach is to build a more comprehensive system that can generalize across various tasks, as demonstrated by AlphaFold 3, which allows us to create models that generalize across chemistry and target space, enabling application to any protein or small molecule without needing fine-tuning.

Before AlphaFold 3, drug designers had to rely on intuition and lengthy lab processes to understand molecular interactions, which is no longer the case.

AlphaFold 3 allows our drug designers to see how their molecule designs interact with protein targets instantly, which is a game changer in drug design.

The future may involve programming proteins as a new form of technology, bridging the gap between computers and human health, which is currently underexploited. With advancements in machine learning, like AlphaFold, we can now predict how DNA sequences fold into proteins in just minutes, potentially revolutionizing how we build machines that interact with the human body and our environment.

AlphaFold has become a standard tool for biology and medical research, with over 2.5 million researchers worldwide using it in their critical work, significantly impacting the scientific community.

More than 2 million researchers worldwide now use AlphaFold, and it has had a significant impact on biology and medical research, but this is just the beginning of AI's potential in science.