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

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.

In the future, foundational models may become highly specialized, such as models specifically designed for chip design or protein folding.

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.

In academia, there is less focus on hardcore drug manufacturing aspects like pharmacokinetics or toxicology, but there is significant interest in molecular glues, machine learning models, and new drug concepts.

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

The models currently have a lot of knowledge but lack the skill level to make significant discoveries, particularly in complex fields like biology.

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.