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.

By 2050, we envision a fully automated lab capable of simulating drug interactions with high accuracy, significantly speeding up drug discovery processes.

Our internal drug design projects focus on immunology and oncology, and we've made significant progress using advanced models.

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.

By 2025, we will be able to design full antibodies and enzymes through advanced AI models, revolutionizing drug development.

Synthetic data plays a crucial role in drug discovery, especially in chemistry, where we can use theoretical models to create scalable molecular simulations and enhance the information content of our datasets.

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

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

Our goal is to solve all diseases by leveraging AI to transform our understanding of biology and chemistry, enabling us to manipulate and craft solutions effectively.