Summary of Key Points
Recently, AI in pharmaceuticals has made two major breakthroughs:
1. The phase three trials of an AI-assisted, custom-made mRNA cancer vaccine developed by Merck and Moderna have shown positive results, potentially making it the first therapeutic cancer vaccine in history.
2. InSili Intelligence’s AI-driven drug development process, covering the entire chain from target discovery to molecular design, has entered phase three clinical trials. This is the first drug in the world where both target identification and molecular design were entirely managed by AI. AI is changing the fundamental logic of drug development, shifting from a traditional “trial and error” approach to using data to predict targets and virtually screen molecules, significantly reducing the early stages of the process. However, AI also has its limitations: the success rate in phase three clinical trials remains low (only 10%) due to the rigid timing and complex management requirements of these trials.
Within the industry, companies have divided into two camps: those that provide services (stable profits but with limited potential for growth) and those that focus on developing their own drug pipelines (high risk, high reward). More companies are adopting a hybrid approach. The key to the future of AI in pharmaceuticals lies in improving efficiency and validating the value of these technologies. 2026 will be a critical year for this industry; while AI cannot eliminate the uncertainties in drug development, it can make them more manageable.
Breakthroughs in AI-Driven Drug Development
These advancements represent significant milestones in the field of AI in pharmaceuticals:
- Merck + Moderna’s mRNA Vaccine: This vaccine, designed with AI assistance, targets malignant melanoma. Each patient’s tumor genes are unique, and AI generates a personalized vaccine based on these genetic mutations, combined with a PD-1 antibody. The positive results of the phase three trials indicate that the concept of “one drug for one patient” is no longer just a dream.
- InSili Intelligence’s Drug for Idiopathic Pulmonary Fibrosis: This is the first drug in the world where the entire development process from target discovery to molecular design was completed by AI, directly moving on to phase three clinical trials. Previously, AI was used as a supplementary tool; now, it can lead the entire early-stage research and development process, marking a historic milestone in the industry.
How AI Accelerates and Improves Drug Development
Traditional drug development is like trying to find a specific “lock” with a “key” – first identifying a target (the key), then trying to match it to a disease (the lock), and finally screening a vast number of compounds. This process is time-consuming (2.5–4 years) and costly. AI improves this process by:
- Identifying the right target: By analyzing genetic and protein data from numerous patients, AI can identify truly relevant targets that were previously overlooked.
- Selecting the right molecules: AI can virtually screen millions of potential molecules, eliminating those with high toxicity and failure risks, eliminating the need for extensive physical experiments.
- Reducing Development Time: InSili Intelligence has reduced the time required to nominate potential drugs from 12–18 months; Jitai Technology’s AI delivery platform has cut the development time for targeted drugs from several years to just 2–3 months; Tsinghua University’s DrugCLIP has increased the speed of virtual screening by a million times.
Challenges Faced by AI
Despite the improvements, AI still faces significant challenges in clinical trials:
- Low Success Rate in Phase Three: While the success rate in phase one trials with AI-assisted drugs is 80–90% (compared to 40–65% for traditional methods), the success rate in phase three trials remains low at only 10%. This is due to the need for long-term observation of patient outcomes, safety assessments, and addressing complex issues such as patient recruitment and trial design.
Different Business Models for AI-Driven Pharmaceutical Companies
As investors start to focus on profitability, companies are adopting different strategies:
- Providing Services: Some companies, like Jitai Technology, offer AI-based contract research and development (CRO) services to other pharmaceutical companies. For example, Jitai Technology generated $800 million in revenue in 2025, a 201% increase, and made its first profit. The advantage is stable cash flow, but the potential for growth is limited.
- Developing Own Drug Pipelines: Other companies, such as InSili Intelligence, develop their own drug pipelines and earn revenue by licensing them to large pharmaceutical companies. InSili Intelligence has signed contracts worth $7.5 billion but still incurred a loss of $43.8 million in 2025. The potential for high returns is significant, but so is the risk of failure.
- Hybrid Models: Many companies, like InSili and Jitai Technology, combine both services and pipeline development. InSili Intelligence’s revenue in the first half of 2026 was $154 million, a 133-fold increase, and its loss decreased by 56%. This approach allows them to generate stable income from services while investing in promising drug pipelines.
The Future of AI in Pharmaceuticals
The focus in 2026 will be on improving efficiency and managing the uncertainties associated with drug development:
- Efficiency: AI can reduce development times by 40–60% and cut costs by millions of dollars, allowing companies to focus resources on the most promising projects. Investors are increasingly recognizing the value of AI in this area; in the first half of 2026, AI-driven pharmaceuticals accounted for 19.2% of innovation funding, making it the third-largest segment of the industry.
- Value of AI: According to experts from EY, AI cannot eliminate the uncertainties in drug development, but it can help in “more accurate pricing” by predicting which drugs are likely to fail, thus avoiding unnecessary waste of resources. This represents a sign of industry maturity – not eliminating risks completely, but managing them more effectively.
In summary, AI in pharmaceuticals is not a miracle, but it indeed makes faster, more accurate, and more personalized drug development possible. Only companies that can effectively leverage AI will be able to thrive in this competitive industry.