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.

In the next few years, we aim to build a comprehensive database of virtual cells to enhance drug target design and therapeutic success rates.

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

Partnerships with Eli Lilly and Novartis have allowed us to tackle challenging problems in drug design, leading to the discovery of new chemical matter.

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.

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 ambition at Isomorphic Labs is to develop a drug design engine that can continuously adapt and apply to new challenges across different diseases, fundamentally changing drug discovery.

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

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.