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.

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

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.

Machine learning for biology is not limited to drug design; it encompasses a broader potential that significantly impacts human lives.

There's increasing acceptance of transitioning from academia to industry, as innovations often stem from academic research, and pursuing entrepreneurship can be a viable path for scientists. Most drugs on the market originate from discoveries made in academia, which are then commercialized by biotech or pharmaceutical companies.

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

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.

Drug design is complex, with numerous bottlenecks before reaching clinical trials, and we need to innovate in this space to improve the process.