The intersection of drug design and machine learning is a new field, and we need to bring together experts from both areas to drive breakthroughs.

In the future, the pharmaceutical industry will integrate AI into drug design, making it an essential tool for biology and chemistry.

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

While there are data constraints in machine learning, we can still make significant progress in biology with the existing data, and there's a massive opportunity to generate new data tailored for machine learning applications.

We aim to create a general drug design engine with AI that can be applied across various disease areas, not just targeting a single indication.

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.

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

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.

My vision extends beyond just creating better drugs; I aim to build a comprehensive understanding of biology at all scales.