AI drug discovery push expands with NVIDIA-Eli Lilly lab and data-driven platforms

NVIDIA and Eli Lilly announced in January a first-of-its-kind AI co-innovation lab to address key challenges in AI drug discovery. The $1 billion partnership signals a new phase in the integration of artificial intelligence into pharmaceutical research and development, as companies use AI-driven platforms to analyze massive biological datasets, run experiments faster, and identify promising compounds with far fewer iterations.

For decades, the drug discovery process has been painfully inefficient. On average, it takes about 2,500 compounds and more than four years just to find one drug worth a clinical trial. In medicinal chemistry, AI models are now used to generate and evaluate thousands of molecular candidates in a fraction of the time it once took to design a handful, while deep learning systems can predict protein structures, binding affinity, and toxicity risks before a compound ever enters a lab.

Climate-driven disease migration, aging populations, and the hard lessons of COVID-19 are shaping the next era of drug development. The World Health Organization has repeatedly warned that warming climates are expanding the range of viruses such as dengue, Zika, and chikungunya into new regions, including parts of the U.S. and southern Europe. Surveillance data from the WHO, the CDC, and national regulators increasingly drives research prioritization when agencies flag a threat and assess whether countermeasures exist or can be developed quickly.

Another shift is happening inside science itself, with a move from single-target drugs to multi-acting therapies. The future lies in multi-acting antivirals and preventive therapies that can address several pathogens through a shared mechanism. A single oral drug that could protect against COVID, RSV, and influenza would fundamentally change how societies manage seasonal and pandemic risk.

Companies such as Insilico Medicine and Recursion Pharmaceuticals are using AI platforms to identify drug targets and advance candidates into clinical trials at unprecedented speed. Moderna has publicly described using AI to optimize mRNA design and manufacturing workflows, shortening development cycles. AI is also transforming clinical trials through algorithms used to identify eligible patients, predict enrollment challenges, and detect safety signals earlier, while AI-driven quality control systems help ensure consistency at scale in manufacturing.

Recursion Pharmaceuticals said the industry average is 2,500 compounds synthesized over 42 months to find one development candidate, while its average is 330 compounds synthesized in 17 months. The company said its drug development operating system integrates robotic wet labs, petabyte-scale biological datasets, and AI models, and that it built whole-genome CRISPR knockout maps in neuronal and microglial cells. It said Roche and Genentech have paid $213 million in fees for access, Sanofi has paid $134 million for five of its discovery programs, full-year 2025 revenue was $74.7 million, it has five clinical programs advancing, more than $500 million in cumulative milestone payments from partners, and a cash runway into 2028. It also reported a 43% median reduction in polyp burden for Familial Adenomatous Polyposis patients as its first clinical proof-of-concept.

Tempus AI said it built a giant library of clinical and molecular data and uses it to advance precision medicine with AI, machine learning, and data analytics, working primarily in oncology, diagnostics, and genomics. The company said trailing revenue reached $1.27 billion, growing roughly 30% annually, with diagnostics feeding the data engine and insights monetizing it through data licensing, AI analytics, and clinical trial matching. It also said its backlog exceeds $1.1 billion and that it had a trailing net loss of $245 million.

By 2030, an estimated 30 percent to 60 percent of the U.S. population will be over the age of 55, according to U.S. Census projections cited in the source material, while China and Japan are aging even faster. Older patients take more medications, face higher risks of drug-to-drug interactions, and respond differently to treatment, adding to pressure for drug development programs built around short-term, mid-term, and long-term horizons.