NewLimit combines artificial intelligence with high-throughput biology in a virtuous cycle. Their AI model, Ambrosia, predicts which gene combinations will be effective. These predictions are then tested in thousands of parallel experiments, which in turn generate massive datasets to further train and refine the AI, accelerating discovery.

Instead of the traditional 'disease-target-drug' approach, Soleil finds compounds that create a desired cellular change first. Only after identifying a promising, well-tolerated molecule with a known cellular mechanism do they use bioinformatics to determine which disease and patient population it's best suited for.

Beyond identifying potential drug targets, Moonwalk uses AI to analyze why some targets succeed in animal models while others fail. By feeding its proprietary biological data into large models, the team gains insights into pathways and mechanisms. This deeper understanding helps prioritize candidates and allows the AI to suggest novel, related targets.

Regeneron identified the main constraint in drug discovery as a lack of validated targets, not a shortage of advanced therapeutic tools. Their genetics engine was created to explore the 90% of the human genome that was untargeted by existing or experimental medicines, aiming to solve this core problem.

Haya's approach redefines the drug target. Instead of focusing on single proteins or pathways, they identify the "causal unit" of disease as the cellular behaviors that dictate how patients feel, function, and survive, and then work backwards to find a target.

A new 'Tech Bio' model inverts traditional biotech by first building a novel, highly structured database designed for AI analysis. Only after this computational foundation is built do they use it to identify therapeutic targets, creating a data-first moat before any lab work begins.

Instead of screening billions of nature's existing proteins (a search problem), AI-powered de novo design creates entirely new proteins for specific functions from scratch. This moves the paradigm from hoping to find a match to intentionally engineering the desired molecule.

By focusing on the phenotypic outcome (cellular stress) rather than a predefined target, Soleil's platform can identify small molecules that modulate proteins considered undruggable by conventional means. Their lead oncology candidate, for example, modulates CCAP2, demonstrating the platform's ability to find novel biology and expand the druggable space.

Profluent CEO Ali Madani frames the history of medicine (like penicillin) as one of random discovery—finding useful molecules in nature. His company uses AI language models to move beyond this "caveman-like" approach. By designing novel proteins from scratch, they are shifting the paradigm from finding a needle in a haystack to engineering the exact needle required.