Generative AI has proven that it can produce text, images, audio, video, and code. The world’s most valuable pharmaceutical company is betting billions that it can produce drugs as well.
What’s new: Pharma giant Eli Lilly agreed to give as much as $2.75 billion to Insilico Medicine, a Hong Kong-based biotechnology company that applies generative AI across its drug-discovery pipeline. Initially, Lilly will pay $115 million for exclusive rights to develop and sell undisclosed drugs that have not yet been tested in humans, while further payments will be tied to developmental, regulatory, and commercial milestones, Fierce Biotech reported. This is the third agreement between the companies following an AI software license in 2023 and a $100 million research collaboration in November 2025.
AI drug-discovery: Founded in 2014, Insilico has used AI to develop 28 candidate drugs, roughly half of which are in clinical trials. The most advanced one, Rentosertib, targets idiopathic pulmonary fibrosis (IPF), a disease in which scarring progressively reduces lung function. A Phase 2a trial (an early, small-scale test of efficacy) showed positive results. A second drug, Garutadustat, which is intended to treat inflammatory bowel disease, entered Phase 2a in January 2026.
How it works: After choosing a disease, Insilico applies proprietary generative models to two stages of drug discovery: identifying which protein to target and designing a molecule to act on that protein.
- To find targets, Insilico uses a tool called PandaOmics to analyze biological datasets, published research, patents, clinical trials, and grant applications. Deep learning models rank candidate targets by relevance to a disease, suitability as drug targets, and novelty. For IPF, PandaOmics identified TNIK, a protein involved in the scarring that characterizes IPF and related diseases, as the top candidate. No one had previously tried to treat IPF by blocking TNIK.
- To design a molecule to block TNIK, the team used Chemistry42. Roughly 30 generative models ran in parallel to produce candidate molecular structures, each one optimized for binding strength, toxicity, solubility, and other properties. Scientists evaluated and refined the output over multiple rounds. The process yielded a lead molecule after Insilico synthesized and tested fewer than 80 compounds. In conventional drug discovery, teams often screen 200,000 to 1 million existing compounds before synthesizing and testing hundreds of candidates.
- The time from identifying targets to synthesizing molecules that are ready for preclinical safety testing took roughly 18 months, compared to a typical five to six years. That pace held steady across more than 20 Insilico programs between 2021 and 2024, each of which synthesized and tested around 60 to 200 molecules to find one preclinical candidate.
Behind the news: Developing a new drug typically takes 10 to 15 years and costs more than $2 billion, and roughly 86 percent of candidates fail to reach approval. A growing number of drug developers apply AI to accelerate the process. A peer-reviewed analysis catalogued 173 AI-enabled drug programs across clinical stages as of mid-2025. Nonetheless, no AI-discovered drug has received regulatory approval. Of the drug candidates that reach Phase 2, 70 percent fail to reach the next phase, including AI-designed drugs from BenevolentAI and Recursion Pharmaceuticals.
Why it matters: Insilico’s pipeline suggests generative AI can tackle one of the hardest problems in science: finding a molecule that binds to a particular protein, is absorbed by the body, isn’t toxic, and helps patients. In Rentosertib’s Phase 2a trial, participants who took the highest dose gained an average of 98.4 milliliters in forced vital capacity (a measure of lung function), while those who took a placebo declined by 20.3 milliliters. That is early but concrete evidence that AI-generated drugs can help patients.
We’re thinking: AI is accelerating drug development, but it remains to be seen whether those accelerated compounds will pass clinical trials at a higher rate than those developed in traditional ways.