AI in pharmaceuticals: from discovery to manufacturing

Artificial intelligence is opening possibilities in pharmaceutical research. Large companies, often called “Big Pharma”, and specialist firms use it to analyse data and support decisions throughout drug development. The aim is to make some processes more efficient and identify better candidates, although cost savings and improved treatments must be demonstrated for each application.

How is AI revolutionizing the pharmaceutical industry? This article analyzes the most surprising applications of AI in the world of pharmaceuticals, highlighting initiatives from companies such as AstraZeneca, Capgemini and Biorelate.

Accelerating Drug Discovery with AI

Discovering and developing a medicine can take more than a decade. AI can analyse large datasets, estimate molecular properties and help prioritise compounds for study. These predictions guide experiments; they do not replace the testing needed to establish safety and efficacy.

For example, AstraZeneca is using advanced AI technologies to predict the behavior of certain compounds in the human body, allowing drug candidates to be identified before expensive experiments are conducted. According to AstraZeneca, AI supports research into drug targets and molecule prioritisation. These are research-support applications, not a guarantee that a candidate will become an approved medicine.

More Personalized Treatments Thanks to AI

Precision medicine seeks to account for relevant differences between patients. AI can contribute by analysing genetic, clinical and other data to identify response patterns. This does not mean it can automatically create an effective, safe treatment for each person: recommendations require evidence and clinical assessment.

Capgemini, a French technology company, is helping pharmaceutical companies take this a step further, integrating AI to develop “next-generation laboratories.” Capgemini’s proposal focuses on connecting laboratory data, tools and processes to support research. Simulating a biological response can guide hypotheses, but does not establish how an individual patient will respond or demonstrate fewer side effects on its own.

Generative AI: Innovating in Drug Development

One promising application of AI in the pharmaceutical industry is generative AI. These models can propose molecular structures for further study. Generating a structure does not establish that it can be synthesised, is useful or is safe. Separately, companies such as Biorelate work with biomedical information and knowledge networks. This should be distinguished from molecule generation: organising evidence and designing candidates are related but different tasks.

One of the approaches discussed is «Graph-as-a-Service», which brings relationships between biomedical entities into a queryable network. Such resources can support hypothesis exploration and evidence retrieval, including in oncology and neurodegenerative disease research.

Improving Clinical Trials with AI

Clinical trials are essential to evaluating medicines. Their complexity depends on many factors, including participant recruitment and follow-up. AI can help identify potential candidates who meet study criteria and organise information, but selection requires clinical review, consent and quality controls.

AstraZeneca describes uses of clinical data and AI tools to support participant identification and trial design. According to the company’s information, the aim is to improve these processes. Benefits and potential savings need to be measured in each study, without assuming that AI will increase its chance of success.

More Efficient Drug Manufacturing

Drug production not only occurs in research laboratories, but also in manufacturing plants. This is where AI is also working its magic. Traditional pharmaceutical manufacturing can be an inefficient process, but AI systems make it possible to optimize every stage, from quality control to mass production.

Companies like Pfizer and Johnson & Johnson have explored digital tools and AI in their operations. Process monitoring and analysis may help detect deviations or reduce waste, but pharmaceutical quality requires controls, validation and oversight; adding AI does not guarantee it.

The Power of AI in Drug Repositioning

The drug repurposing is another area where AI is making a difference. This practice consists of finding new applications for existing drugs. Using advanced analysis of clinical and historical data, AI is identifying unexpected uses for approved drugs, accelerating the time needed to bring treatments to market.

The ability of AI to identify hidden patterns in data from clinical trials, medical histories and previous studies has made it possible to discover which drugs for one disease could be effective in treating completely different ones. A study published in the journal Frontiers in Medicine describes how data analysis can suggest new indications for existing medicines. Each hypothesis requires experimental and clinical evaluation; approval for one disease does not establish effectiveness for another.

The Future of AI in the Pharmaceutical Industry

AI is still in its early stages within the pharmaceutical industry, but its potential is huge. As machine learning, generative AI, and data analytics technologies continue to advance, we will see even more impressive innovations. AstraZeneca, Pfizer and Johnson & Johnson are already at the forefront of this transformation, demonstrating that collaboration between AI and pharmaceutical research is bearing fruit.

The goal is for these tools to support more effective treatments and better identify who may benefit. Reaching that goal depends on data quality, appropriate trials and rigorous assessment of benefits and risks.

Why Should You Care?

While AI in Big Pharma is a fascinating convergence of technology and science, its real impact is on patients. A tool’s value is measured not just by data-processing speed, but by the quality of its evidence and its contribution to care. For those following AI in health, the central question is: what improvements can we demonstrate, and for whom? The aspiration is a form of medicine in which AI AI helps improve processes and contributes to a better quality of life to patients.

Collaboration between technology and pharmaceutical research opens promising avenues. Turning them into real improvements requires experimentation, transparent evaluation and access to treatments that ultimately prove useful. That is the distance between an interesting prediction and a benefit for patients.