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.

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.

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

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.

The training of AlphaFold 3 involved overcoming challenges in tokenizing various molecular types, including proteins, DNA, and small molecules, to accurately predict their structures and interactions.

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.

Our sister company, Isomorphic Labs, is developing technologies around AlphaFold to design drug compounds that bind to specific parts of protein surfaces, which is crucial for understanding diseases.

To achieve transformative drug design, we need several more breakthroughs similar to AlphaFold, focusing on different core concepts of biology and chemistry.