AI for Science Demands Reasoning, Not Just Data

In 2024, Demis Hassabis and John Jumper were awarded the Nobel Prize in Chemistry for their work in developing AlphaFold, Google DeepMind's artificial intelligence for protein structure prediction. This marked a significant breakthrough in the scientific community, as AI solved the long-standing challenge of protein structure prediction, a problem that had persisted for over 50 years.

Despite AlphaFold's success, artificial intelligence-driven scientific research is increasingly emphasizing the need for reasoning capabilities beyond mere data learning.

Considering that the construction of protein databases involved 53 years of international collaboration and an estimated $21 billion, there is a growing argument that for AI to further accelerate scientific discovery, it must possess abilities akin to human researchers' reasoning processes, rather than simply learning from data.