For intractable diseases like Parkinson's, the IGI takes an 'end-to-end' approach: building better disease models, discovering root causes, and simultaneously exploring multiple treatment modalities like direct CRISPR edits, cell therapies, and microbiome interventions. This tackles the entire problem, not just one piece.

The next major AI breakthrough will come from applying generative models to complex systems beyond human language, such as biology. By treating biological processes as a unique "language," AI could discover novel therapeutics or research paths, leading to a "Move 37" moment in science.

Gene editing pioneer David Liu is developing a platform that could treat multiple, unrelated genetic diseases with a single therapeutic. By editing tRNAs to overcome common nonsense mutations, one therapy could address a wide range of conditions, dramatically increasing scalability and reducing costs.

CRISPR's origins lie in basic microbiology. Scientists studying unusual repeating DNA sequences in bacteria discovered they were part of an adaptive immune system. Bacteria use CRISPR to recognize and cut the DNA of invading viruses (bacteriophage), a mechanism that was then repurposed for gene editing.

The next frontier in preclinical research involves feeding multi-omics and spatial data from complex 3D cell models into AI algorithms. This synergy will enable a crucial shift from merely observing biological phenomena to accurately predicting therapeutic outcomes and patient responses.

Following the success of AlphaFold in predicting protein structures, Demis Hassabis says DeepMind's next grand challenge is creating a full AI simulation of a working cell. This 'virtual cell' would allow researchers to test hypotheses about drugs and diseases millions of times faster than in a physical lab.

Patrick Collison believes we can finally cure complex diseases because biology now has a complete 'Turing loop': advanced sequencing to 'read' biological data, neural networks to 'think' about it, and CRISPR to 'write' changes by perturbing cells. This combination provides the necessary toolset for breakthroughs.

Bob Nelsen believes the industry overestimates AI's short-term impact and underestimates its long-term potential. He predicts that once a critical data threshold is met, AI models won't just accelerate drug discovery but will fundamentally invent new biology, creating a sudden, paradigm-shifting moment.

Afeyan proposes that AI's emergence forces us to broaden our definition of intelligence beyond humans. By viewing nature—from cells to ecosystems—as intelligent systems capable of adaptation and anticipation, we can move beyond reductionist biology to unlock profound new understandings of disease.