The model can interpret genetic mutations and provide insights into their functional consequences, which is state-of-the-art in computational biology.

Our model, EVO, is designed to predict the next base in a DNA sequence, which can determine health outcomes, such as the presence of sickle cell anemia mutations.

The ability to predict the next word in a sequence is deceptively complex and requires a model of reality, which is a significant achievement in AI.

The model learns from vast datasets of genomic sequences, allowing it to identify biologically meaningful patterns that can lead to new insights in biology.

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 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.

Understanding how molecules interact with all proteins in the body is crucial, as this knowledge can help predict toxicity and improve 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 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.