Counterintuitively, Nobel laureate John Jumper's path to AI began not with abundant resources, but as a way to use sophisticated algorithms to compensate for a lack of computational power for protein simulations during his PhD.
The DeepMind team was surprised that their specific software became a ubiquitous tool. They expected to solve a grand challenge and then have others build useful systems based on the concepts, not use the original artifact directly.
John Jumper contends that science has always operated with partial understanding, citing early crystallography and Roman engineering. He suggests demanding perfect 'black box' clarity for AI is a peculiar and unrealistic standard not applied to other scientific tools.
AlphaFold's success in identifying a key protein for human fertilization (out of 2,000 possibilities) showcases AI's power. It acts as a hypothesis generator, dramatically reducing the search space for expensive and time-consuming real-world experiments.
Users on Twitter figured out how to use AlphaFold to predict protein-protein interactions—a key capability the DeepMind team was still developing separately. This highlights the power of open models to unlock emergent capabilities discovered by the community.
A classical, bottom-up simulation of a cell is infeasible, according to John Jumper. He sees the more practical path forward as fusing specialized models like AlphaFold with the broad reasoning of LLMs to create hybrid systems that understand biology.
John Jumper uses an analogy to explain the leap in complexity from prediction to design. Predicting a protein's structure is like recognizing a bicycle's parts. Designing a new, functional protein is like building a working bicycle—requiring every detail to be correct.
Despite AI's power, 90% of drugs fail in clinical trials. John Jumper argues the bottleneck isn't finding molecules that target proteins, but our fundamental lack of understanding of disease causality, like with Alzheimer's, which is a biology problem, not a technology one.