The pharmaceutical industry is investing more heavily in artificial intelligence (AI) as drugmakers look for better ways to discover new medicines. Major companies are joining forces with AI-focused biotechnology businesses and technology providers to identify promising drug targets, study disease biology and improve the early stages of drug development.
Recent partnerships involving AstraZeneca, Eli Lilly and Alnylam Pharmaceuticals show how quickly this area is developing. However, while the investment is growing, the real test will be whether AI can consistently help researchers develop safe and effective medicines for patients.
One of the latest examples came on October 15, 2026, when AstraZeneca announced a strategic collaboration with insitro, a biotechnology company that uses AI and machine learning to study human biology. The partnership will focus on finding new treatments for metabolic dysfunction-associated steatohepatitis (MASH), a serious liver disease, and cardiomyopathies, a group of conditions that affect the heart muscle. Both areas present challenges for drug researchers, making the search for new treatment options important.
Under the agreement, insitro will receive $40 million upfront and could receive approximately $1.46 billion in potential discovery, development and commercial milestone payments, along with royalties on future sales. These additional payments depend on the programmes reaching specified milestones and are not guaranteed.
The companies plan to use insitro’s Virtual Human platform, which combines large amounts of human and cellular data with AI systems. The goal is to understand the biological processes that contribute to disease and identify targets that could lead to new medicines. AstraZeneca will have exclusive worldwide rights to advance selected programmes and will take responsibility for future clinical trials.
The partnership reflects a wider shift in how pharmaceutical companies approach early-stage research. Instead of relying only on traditional laboratory experiments to explore potential drug targets, researchers can use AI to analyse large datasets, identify patterns and decide which scientific questions are worth investigating first.
Another major development came in January 2026, when Eli Lilly and NVIDIA announced a joint AI innovation laboratory. The companies said they would invest up to $1 billion over five years in infrastructure and research to address challenges in drug discovery and manufacturing.
The collaboration brings together Lilly’s experience in medicine research and development with NVIDIA’s computing technology and AI expertise. One of its aims is to connect computer-based research with laboratory experiments so that information from each can help improve the next stage of work.
For example, an AI system could help researchers assess which molecules are worth testing, while laboratory results could provide new information to improve the system’s predictions. This approach may help research teams explore more possibilities and focus their time and resources on the candidates with greater potential. The results will still need to be confirmed through scientific experiments.
AI is also attracting interest in the development of RNA-based medicines. In June 2026, Alnylam Pharmaceuticals announced a collaboration with Inceptive, an AI-focused biotechnology company, in a deal that could be worth up to $2 billion. Alnylam agreed to pay $30 million upfront, with additional payments linked to research, regulatory and commercial milestones.
The collaboration aims to combine Alnylam’s experience in RNA interference (RNAi) medicines with Inceptive’s AI models to help design and select promising drug candidates. RNAi medicines work by reducing the production of specific proteins linked to disease. The companies hope the partnership will improve the process of identifying and advancing new RNA-based treatments.
These deals highlight why pharmaceutical companies are interested in AI. Developing a medicine is expensive and time-consuming, and many potential drug candidates fail because they do not show the expected effects or raise safety concerns. AI may help researchers make better decisions during the early stages by comparing biological data, predicting the properties of molecules and narrowing the number of candidates that need laboratory testing.
However, AI does not remove the main challenges of drug development. A computer model may predict that a molecule could interact with a disease-related target, but this does not prove that the molecule will work in the human body. Researchers must still investigate its safety, confirm its biological effects and establish whether it can provide meaningful benefits in clinical trials.
A review published in Nature Reviews Drug Discovery has also highlighted the gap between the promise of AI-based drug discovery and the evidence needed to demonstrate reliable results. Questions around independent validation and the limited public detail available on some industry projects remain important.
For this reason, the success of AI drug discovery should not be measured only by investment figures, the number of partnerships or the speed at which a computer generates potential molecules. More meaningful measures will include the quality of drug candidates identified, their progress through development and, ultimately, their safety and effectiveness in patients.
For biotech companies, these partnerships can provide access to funding, advanced computing resources and the development experience of established pharmaceutical businesses. For large drugmakers, working with specialist AI companies offers a way to bring new tools into research without having to develop every technology internally.
AI is unlikely to replace pharmaceutical scientists. Its greater value may come from helping them ask better questions, identify promising opportunities and use laboratory resources more effectively. As these collaborations progress, clinical and experimental results will determine whether the technology delivers on its promise.
The growing investment suggests that AI will play an increasingly important role in pharmaceutical research. The next major milestone, however, will not be another multibillion-dollar agreement. It will be clear evidence that AI can help turn scientific discoveries into medicines that improve patients’ lives.