From drug discovery to development to delivery, AI is revolutionizing pharmaceutical innovation. The use of AI has already resulted in accelerated drug R&D, streamlined clinical trials and faster regulatory approval, and the technology will undoubtedly continue to power breakthroughs.
This article explains how AI in drug development works, how leading pharmaceutical companies like Sanofi are using AI, and what it means for the future of pharmaceutical innovation.
The Growing Role of AI in Drug Innovation
You may be surprised to learn that the use of AI in drug development began back in the 1980s. At the time, AI applications in drug discovery were limited to basic computer models, mostly used for chemical structure prediction and molecular modeling.
But today, AI is a foundational tool in pharma R&D. Traditional drug discovery methods are time-consuming and costly – it often takes 10 to 15 years and billions of dollars to bring a new treatment to market. AI helps break these barriers by allowing for faster and smarter decision-making at every stage of the process.
AI’s importance in drug development is being proven in clinical trials, as discussed at VivaTech 2025 in the session AI-Powered Drug Innovation: From Discovery to Development:
“Today, 21 AI-designed drugs have made it through phase I trials, and a couple have made it through phase II. This is about 2 or 3% of all of the drugs that get through phase I every year,” said Mathilda Strom, founding COO at Bioptimus, an AI startup focused on developing foundational models for biology. “But what’s interesting is that the AI-designed drugs tend to be more successful in their phase I – about 80 to 90% success rate versus anywhere from 50 to 70% success rate for non-AI drugs. So, already there is starting to be proof that AI can drive better results, so that you have fewer trials and more efficiency and more accuracy in what you’re doing.”
One of AI’s biggest strengths lies in its ability to process and analyze enormous volumes of data. Pharmaceutical companies use machine learning to sift through genomic data, protein structures, clinical trial outcomes, and medical records. AI’s ability to quickly analyze huge datasets powers predictive modeling, a statistical technique that uses past data to predict future outcomes. Predictive modeling helps drug researchers identify promising compounds, eliminate weak candidates early, and design better-targeted therapies.
From Molecules to Market: AI in Action
AI-powered tools are now being leveraged across nearly every stage of the drug pipeline to automate and improve processes that once relied heavily on manual trial and error experimentation. Major examples of AI in action in the drug innovation process include:
Compound Screening & Target Identification
AI drastically reduces the amount of time needed to search for viable drug candidates by analyzing enormous amounts of chemical and biological data at scale. Algorithms trained on past pharmaceutical successes and failures can quickly identify molecules with therapeutic potential. This process, called AI drug discovery, is especially useful in targeting complex or rare diseases where traditional methods have hit a dead end.
Deep learning models can also predict how specific molecules will behave inside the human body. These AI models can then identify the best molecular structures and suggest potential modifications to improve efficacy or reduce toxicity. This makes the process faster and safer.
Patient Response Prediction & Clinical Trials
Another crucial AI application in drug innovation is predicting how different patient groups may respond to a drug. AI enables patient-specific simulations, allowing researchers to design more precise clinical trials and minimize risks. This has resulted in more successful trial outcomes and faster regulatory approval.
For example, a team at Stanford University developed machine learning technology to model real-time cardiovascular simulations. This allows researchers to generate non-invasive, accurate, and patient-specific blood flow visualizations for cardiac studies. This AI advancement has far-reaching potential for fighting the leading cause of death in the U.S.
AI models are also highly useful in clinical trials. Machine learning can help select ideal candidates for participation in trials by detecting biomarkers associated with drug response. This accelerates timelines but also increases safety and efficacy, one of the core goals when it comes to pharmaceutical innovation with AI.
Case Study – Sanofi’s Use of AI in R&D
Global pharmaceutical leader Sanofi has emerged as a leader in integrating AI-powered biopharma across its research and development processes. At VivaTech 2025, Sanofi showcased how it’s leveraging advanced analytics and machine learning to radically transform its drug pipeline.
Here are just a few of the many ways Sanofi uses AI in R&D:
Large Language Models: Sanofi scientists use the company’s large language model CodonBERT to develop mRNA-based vaccines and therapies faster than ever. Sanofi says that CodonBERT has been pre-trained on 10 million mRNA sequences and has cut down mRNA design time by 50%.
Shortening the Diagnostic Journey: AI helps Sanofi shorten the discovery cycle for potential treatments for immunological and neurological conditions. The company uses deep neural networks and other technologies for target identification, drug and molecule design, improving clinical trials, and more.
Improving Manufacturing Processes: Sanofi uses an AI-powered yield analytics platform called SimplY to provide data insights on batch performance. This information allows manufacturing and supply teams to spend less time crunching data and more time improving yield, getting treatments to patients faster, and improving cost efficiency.
Beyond Sanofi, other pharmaceutical companies such as Pfizer, Roche, GSK, and smaller biotech firms like Recursion and Insilico Medicine are also discovering ways to redefine the R&D process with AI.
The Future of Pharmaceutical Innovation with AI
As AI continues to advance, the technology’s role in healthcare and pharmaceuticals is set to expand dramatically. The long-term vision includes more personalized therapies, faster time-to-market, and a shift from reactive to proactive care.
Reducing Costs & Expanding Access
One of the most exciting possibilities of AI in biopharma is democratizing drug development. With reduced R&D costs, companies can lower drug prices and increase global access to essential medicines. AI could also empower smaller biotech startups to compete with pharmaceutical giants by leveling the innovation playing field.
Current Limitations & Research Opportunities
Despite the promise and excitement, AI in pharma is not without challenges. AI models are powerful, but often difficult to explain. In a high-stakes process like drug development, understanding is crucial for regulatory approval and public trust. Data privacy, model transparency, and global regulation standards still need to be addressed.
Additionally, access to high-quality, standardized data remains a bottleneck. Collaboration between companies, governments, and research institutions will be key to unlocking the full potential of AI in this space.
Still, the momentum and impact of AI in drug innovation is undeniable. As machine learning models grow more sophisticated and datasets more complete, AI will expand its applications and role in pharmaceutical R&D.
From the lab to the hospital to the patient’s home, AI in drug discovery and development produces faster discovery, more precise targeting, better patient outcomes, and more efficient trials. The future of pharmaceutical innovation with AI is already here, and it's accelerating.
For a wider look at how AI is transforming the healthcare industry, check out this article: AI in Healthcare: How AI Is Saving Time, Money, and Lives
