A blueprint for data-driven molecule engineering
Alternatively titled: How to Turn Data into Molecules at the Speed of Thought.
Hello fellow datanistas!
I’ve been pondering how cross-functional teams in biotech can accelerate molecule discovery to the 'speed of thought'. In my latest blog post, I dive into this very question through the story of a fictitious startup, Catalyst Therapeutics.
In this post, I share insights from Catalyst's journey, highlighting the importance of integrated experimental design, statistical modeling, and machine learning. You'll meet the core team—Maya, Dev, and Sophie—and learn how their collaborative approach to experimental design set the foundation for success. From controlling experimental variables to building predictive models, this post is packed with practical lessons for biotech data science teams.
Catalyst's story illustrates key principles for biotech founders: integration over specialization, the importance of experimental design, and the balance between computation and intuition. These lessons are crucial for any team looking to harness data for molecule engineering.
I invite you to read the full post here. If you find it insightful, please share it with others who might benefit from these lessons.
Happy reading!
Eric