Drug design is complex, requiring an understanding of how small molecules interact with proteins, modulating their functions in the body.

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.

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

There's a significant opportunity in generating in vivo data, which is currently limited, to better understand drug interactions in real biological systems, and we're seeing breakthroughs in data-generating technology in biology and chemistry that will significantly impact how we model these fields.

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.

Programming biology goes beyond drug development; it involves understanding how hormones can influence thoughts, feelings, and behaviors in powerful ways.

Machine learning for biology is not limited to drug design; it encompasses a broader potential that significantly impacts human lives.

Generative models are essential for navigating the vast molecular space, allowing us to explore potential designs that could lead to effective therapeutics.

The model can also predict the next amino acid residue, which is crucial for determining the functionality of enzymes in biological processes.