In the high-stakes world of modern medicine, finding a cure should be mathematically impossible. Researchers estimate there are 10^60 possible chemical compounds—or 10 to the 60th power, a number greater than the stars in the observable universe—yet only a microscopic fraction will ever become viable medicines. Now, a convergence of big pharma legacy and venture capital investment is betting billions that artificial intelligence can finally decode this infinite complexity.
One of the leaders of this movement is Isomorphic Labs, a drug-design company spun out of Google parent Alphabet. In a signal of the market’s appetite for AI-driven biotechnology, the company secured a massive $600 million Series A funding round in March 2025. The round was led by Joshua Kushner’s Thrive Capital, with participation from Google Ventures, validating a shift in investment philosophy: treating biology less like a game of chance and more like an engineering problem.
“No one would visualize designing an airplane today by hand, nor would you want to fly an airplane designed by hand,” Thrive Capital partner Vince Hankes told Fortune‘s Allie Garfinkle. “But all of our drugs are designed like that.” In the future, he predicted, drugs should all be designed “with robust software and intelligence and simulation, just like we design airplanes today.”
The Catalyst: AlphaFold
The latest financing push surrounding this sector stems from the success of AlphaFold 2, an AI system that solved the “protein folding problem” by predicting 3D protein structures from DNA sequences. This breakthrough, which earned Isomorphic founder Demis Hassabis a Nobel Prize in 2024, offered the first seismic proof that AI could compress biological research processes that once took years into mere minutes.
The traditional drug discovery process is grueling: it typically takes over a decade and costs more than $2 billion to bring a new medicine to market, with a staggering 90% failure rate during clinical trials. Historically, chemists have relied on brute force, boiling sludge and running endless lab tests to find compounds that work, a process Isomorphic’s Chief Scientific Officer Miles Congreve compares to “Whac-a-Mole.”
To change these odds, pharmaceutical giants are partnering with tech firms to target “undruggable” diseases. Isomorphic has established partnerships with Eli Lilly and Novartis, the latter expanding its collaboration in 2025. These alliances focus on cracking the codes of protein mutations prevalent in pancreatic, lung, and colorectal cancers—targets that have historically resisted treatment.
The Reality Check: ‘Wet-Lab’ Work
Despite the influx of capital and computing power, the transition from algorithmic prediction to clinical cure remains perilous. As of January 2026, Isomorphic Labs has yet to move a drug into the critical clinical trial phase, though competitors like Insilico have candidates in trials in China.
The integration of AI into physical science faces friction. “It’s humbling when rubber meets the road, with real scientific processes and real wet-lab work,” said Isomorphic President Max Jaderberg. Even with the best software, biological unpredictability persists. Fiona Marshall, president of biomedical research at Novartis, suggested that while AI could eventually shave five years off the average discovery timeline, human safety trials cannot be algorithmically bypassed.
Looking Toward a Virtual Future
The ultimate ambition of this big pharma and VC bet goes beyond merely speeding up the current system. Hassabis said he envisions building a “virtual cell” capable of predicting how interventions will play out before they effectively touch a patient. The goal is to build a scalable process that generates “dozens of drugs each year,” moving the industry toward a future of personalized medicine where patients might one day phenotype their specific diseases at a pharmacy to receive custom-designed treatments.
For now, however, the industry sits at a pivotal juncture, armed with billions in funding and powerful code, trying to prove that it can turn the chaotic randomness of biology into a consistently solvable equation.
For this story, Fortune journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.
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