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

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.

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.

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.

The promise of AI in drug discovery lies not just in predictive power but in the ability to simulate experiments in a parallelized fashion, which could significantly reduce the time and cost involved in traditional methods.

In today's AI landscape, many subject matter experts have already explored problems deeply, and collaborating with them can accelerate the validation and building process of new ideas.

Multidisciplinary research will be crucial in the next decade, combining expertise in areas like AI or machine learning with fields such as biology, chemistry, or mathematics.

Building a multidisciplinary team is challenging because there are more people in machine learning and biology than those who are truly bilingual in both fields.