While many biotech companies may identify as “AI-first”, machine learning is often one of three components that come together to create coherent solutions to problems.
The first and most crucial component is the company’s main differentiator. It may be a new way of approaching biology or an asset/IP in the more traditional biotech sense. Everything else is built around this component with the ultimate goal of supporting its development.
This may be probing the immune system through aging biology (Spring), growing brain organoids and measuring drug effects (Herophilus), or laser editing to reprogram cells (Cellino).
The second component is the data generator, the scale enabler. It allows for running controlled experiments and measuring thousands of interactions. Automation and robotics play a major role here.
These may be physical experiments including high throughput screening and long-term culture with continuous monitoring, or even purely computational in the form of physics-based molecular simulations.
Finally, we have machine learning - often referring to data science in the broader sense. This is where data is used to create value and make decisions. It may be data analysis, trend identification, a model that captures some essence of the data, or even plain storytelling.
You may also view these three components as part of a continuous loop, a discovery machine of sorts. A scientific belief that guides an experiment, followed by data analysis that ultimately informs how this belief should be altered for the next iteration through the loop.
This is simply how science has always been done. However, our recent ability to perform large scale experiments and analyze data at unprecedented fidelity is what enables rapid efficient loops with tighter feedback cycles.
Without a science-driven hypothesis as a company’s core brand, together with a means of generating data at scale in controlled environments, there would be no utility for machine learning.