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

The future of drug development will involve a holistic approach, integrating functional medicine with traditional drug therapies to enhance overall health.

Biology is fundamentally digital; every organism is made up of sequences of DNA, and understanding how to program that DNA allows us to design biology to perform specific tasks.

My vision extends beyond just creating better drugs; I aim to build a comprehensive understanding of biology at all scales.

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

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

Understanding how molecules interact with all proteins in the body is crucial, as this knowledge can help predict toxicity and improve drug design.

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.

We need to conduct more human experiments to improve our understanding of drug effects and their mechanisms in the human body.