Novartis CEO on AI drug development: the impact and what it means for patients
Photo: N43 and HermesThe head of one of the world's largest pharmaceutical companies says artificial intelligence will fundamentally change how drugs are discovered, tested, and brought to market. The promise is faster, cheaper, and more targeted therapies, but the timeline is measured in years not months.
01How Novartis is using AI in drug development
Novartis is applying artificial intelligence across the drug development pipeline, from target identification and molecular design to clinical trial optimization and pharmacovigilance. The company has invested in AI platforms for protein structure prediction, generative chemistry, and patient stratification.
The goal is not to replace scientists but to augment them. AI can screen vast chemical libraries, predict how molecules will behave, and identify patient populations most likely to respond. This allows researchers to focus on the most promising candidates earlier, reducing the attrition that drives up cost and delay.
02The pharmaceutical industry transformation
The pharmaceutical industry is in the early stages of a transformation driven by data, computation, and biology converging. Companies that once relied on empirical screening and trial-and-error chemistry are building digital capabilities, hiring data scientists, and partnering with AI startups.
Novartis is not alone. Roche, AstraZeneca, Sanofi, and others have made significant AI investments. The transformation is uneven, with some companies integrating AI deeply into discovery and others applying it narrowly to specific tasks. The competitive landscape is shifting as technology and biology become interdependent.
03What AI means for drug timelines and costs
Drug development traditionally takes 10 to 15 years and costs over a billion dollars per approved therapy, with high failure rates. AI has the potential to compress early discovery from years to months and to improve the probability that a candidate succeeds in later stages.
The savings will not appear immediately. AI-discovered drugs still need the same clinical trials, regulatory review, and manufacturing scale-up. The impact on timelines and costs will be gradual and will show up first in preclinical phases, where the most repetitive and data-intensive work happens.
04The clinical trial implications
Clinical trials are the most expensive and time-consuming part of drug development. AI is being used to optimize trial design, identify suitable sites, predict enrollment, and monitor safety signals. Digital biomarkers and real-world data are supplementing traditional endpoints.
The biggest opportunity is in patient stratification. By analyzing genetic and clinical data, AI can identify which patients are most likely to respond to a therapy, enabling smaller, faster, and more targeted trials. This is particularly important for precision medicine and rare disease programs where patient populations are small.
05How AI changes drug discovery strategy
AI enables a shift from screening known chemistry to designing novel molecules with desired properties. Generative models can propose structures that no human has synthesized, optimized for potency, selectivity, and drug-like properties. This expands the chemical space that drug hunters can explore.
Strategy also changes around targets. AI can help identify and validate new biological targets by mining large datasets for disease associations and mechanisms. This opens therapeutic areas that were previously undruggable or poorly understood, though validation remains the bottleneck.
06The competitive landscape in pharma AI
Pharmaceutical AI investment is concentrated among the largest companies, which have the data, resources, and pipeline scale to justify it. The chart below shows the scale of investment by leading pharma companies and the gap between the top spenders and the rest of the industry.
Competition is not only between pharma companies. Technology firms, AI startups, and academic centers are all building drug discovery platforms. The question is whether incumbents integrate AI as a tool or whether new entrants disrupt the model by being AI-native from the start.
07What patients can expect
Patients should not expect overnight change. AI-discovered drugs in the pipeline today will reach the market in the 2030s. What they can expect is a gradual increase in the number of targeted therapies, faster development of drugs for rare diseases, and better matching of treatments to patient profiles.
The more immediate impact is on diagnosis and care. AI tools for clinical decision support, imaging analysis, and patient monitoring are already being deployed. The drug development revolution will arrive in steps, and patients will see benefits first in areas where AI accelerates existing processes rather than replacing them.
