2 challenges related to scaling scientific method
I see 2 challenges in the process of the science.
First, verifying a hypothesis needs a long time. According to the book - 2nd chapter, the author David Sinclair  and his professor Guarente discovered had a hypothesis that is about a root cause of aging: That is like Young => Injured genome => Disturbed Epigenetics => The cell's identify is lost => The cell is old => Disease => Death. Then they discovered one thing to prove their hypothesis by using yeast, and published it on the popular science magazine "Cell" in December 1997. Then they tried to verify the hypothesis by using Mammal's cell for 10 years.
Second, the process to find a chemical molecular that has an ideal structure that is close to a molecular that is not the best in the purpose, can be improved by AI computing. I can not remember where the part is in the book. But in the book, there was a mentioned biologist who remembered a lot of chemical molecular with the structure and characteristics. And the biologist found the best effective chemical closer with the another molecule by their skills. I think this process can be improved by the AI technology.
IBM Research working on the challenges
I see that IBM Research is doing some activities to improve the challenges. For example, according to , this might accelerate the process of "wet work" converting it to "dry work" by automating the process by computing.
There are also interesting articles from IBM Research in World Economic Forum .
-  Lifespan book: https://lifespanbook.com/ / David Sinclair
-  https://en.wikipedia.org/wiki/David_Andrew_Sinclair
-  IBM Launches Free AI Tool in the Cloud for Predicting Chemical Reactions
-  IBM has built a new drug-making lab entirely in the cloud - AI, robotics, and the cloud are allowing scientists to run chemistry experiments remotely: https://www.technologyreview.com/2020/08/28/1007737/ibm-ai-robot-drug-making-lab-in-the-cloud/
-  https://phys.org/news/2020-09-ibm-ai-based-chemistry-lab.html
-  Science & Technology Outlook 2021: https://www.research.ibm.com/downloads/ces_2021/IBMResearch_STO_2021_Whitepaper.pdf
-  https://www.research.ibm.com/
-  https://www.weforum.org/agenda/authors/dario-gil