As AI-designed drugs enter clinical phases, we must rethink how we engage with regulatory bodies and streamline the clinical trial process.

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

There's significant innovation in designing clinical studies to quickly gather initial data from human trials, allowing for faster assessments of whether a drug is likely to work.

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

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.

Building a diverse team of experts in AI, chemistry, and biology is essential for questioning traditional approaches and fostering innovation in drug design.

The focus for biotech companies is on de-risking their technology by obtaining experimental data and developing a viable therapeutic intervention, which is crucial before entering clinical trials.

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.

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