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

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

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.

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

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.

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

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.

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

There's significant potential for breakthroughs by merging different subject areas, and I've always aimed to be multidisciplinary in my own work, integrating neuroscience, machine learning, and computer science.