AI modeling transforms drug development from a numbers game of screening millions of compounds to an engineering discipline. Researchers can model molecular systems upfront, understand key parameters, and design solutions for a specific problem, turning a costly screening process into a rapid, targeted design cycle.

Wet lab experiments are slow and expensive, forcing scientists to pursue safer, incremental hypotheses. AI models can computationally test riskier, 'home run' ideas before committing lab resources. This de-risking makes scientists less hesitant to explore breakthrough concepts that could accelerate the field.

The most significant breakthroughs will no longer come from traditional wet lab experiments alone. Instead, progress will be driven by the smarter application of AI and simulations, with future bioreactors being as much digital as they are physical.

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.

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.

Instead of patenting a specific molecule, Alt-Pep underwent a decade-long process to patent the novel alpha-sheet protein structure itself. This unconventional IP strategy gives them a powerful, defensible platform applicable across numerous amyloid diseases, not just a single target composition.

CZI's virtual cell models act as a computational "model organism," enabling scientists to run high-risk experiments in silico. This approach dramatically lowers the cost and time required to test novel ideas, encouraging more ambitious research that might otherwise be prohibitive.

The long-term vision for Alt-Pep's diagnostic extends beyond symptomatic patients or those with family histories. The goal is for it to become a routine screening assay, administered annually to the general population to catch the disease at its earliest molecular stages, changing the paradigm from treatment to prevention.

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.