From finding promising drug candidates to optimizing clinical trials and scaling up manufacturing, AI is reshaping the entire drug development journey, leading to faster development, lower costs, shorter timelines, and, most importantly, quicker access to life-saving treatments for patients.
Regulators are shaping this transition too, with AI-specific guidance and updated expectations for cGMP-compliant environments. For quality, regulatory, and R&D teams in pharma and biotech, this makes it increasingly important to understand where AI can deliver value, where to adopt it first, and what controls and oversight are needed to stay compliant.
Traditional drug development is still slow, expensive, and prone to failure. The next advantage will not come from using AI everywhere, but from knowing where it can accelerate the work, where evidence must follow the model, and where human judgment must remain central.
In this article, you'll learn why traditional drug development takes so long, where AI is already being used across the pipeline, what the benefits are, and what regulators expect from teams adopting it.
The 5 stages of drug development
Drug development typically moves through five major stages before a medicine reaches regulatory review, each with its own risks and bottlenecks.
- Target identification and selection: researchers identify a disease mechanism, protein, or gene worth targeting, sifting through large volumes of biological data to surface connections a manual review would likely miss.
- Target validation: a critical filter. Teams confirm that acting on the target produces a real therapeutic benefit before committing further resources.
- Lead compound identification and optimization: high-throughput screening tests thousands to millions of compounds against the validated target. Only a handful advance, and those are refined for effectiveness and safety.
- Preclinical testing: promising compounds are evaluated in lab (in vitro) and animal (in vivo) models to assess safety, toxicity, and behavior before human trials.
- Clinical trials: the longest, most expensive stage. Human volunteers are tested across phase I, II, and III to confirm safety, efficacy, and dosing.
In the US, the available evidence is compiled after these five stages for FDA regulatory review and drug approval, where the regulatory agency evaluates the data generated throughout development.
As a drug candidate moves through these stages, the uncertainty decreases as researchers conduct testing. But, the cost of being wrong increases as more evidence, time, and resources are invested in it. This is why finding reliable signals as early as possible matters throughout the drug development journey.
This also raises an important question: where can AI make a meaningful difference in drug development? If AI can help researchers identify patterns, assess evidence, and surface potential problems earlier, it could help teams make better-informed decisions before the cost and complexity of the next stage increase.
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Why drug development takes 10+ years
Traditional drug development is long and risky because of a combination of scientific, regulatory, and operational challenges, all aimed at protecting safety and efficacy.
According to a report in Nature Reviews Drug Discovery, bringing a new medicine to market takes 10 to 15 years on average and costs over $2.5 billion, from early discovery through human proof-of-concept studies to regulatory approval.
One of the biggest hurdles is a high attrition rate at every stage:
- For every 10,000 compounds tested, only 10 to 20 advance to development, and just a fraction of those ever reach clinical trials (PPD Inc, 2025).
- Nearly 80% of clinical trials fail to meet enrollment timelines, and up to 50% of research sites enroll one or no patients.
- Approximately 90% of drug candidates fail during clinical development (Sun D. et al, 2022).
Those clinical-stage failures can be broken down by various causes:
Rising protocol complexity adds to the timeline too: amendments needed to address evolving scientific understanding or regulatory requirements can cause significant delays and increase costs. Coordinating across multiple global regulatory frameworks adds further length.
By the time a drug is approved, much of its patent life may already be gone, which can put pressure on companies to recover billions in investment quickly.
How AI is accelerating the journey of drug development
AI in drug development refers to the use of machine learning, generative AI, and other computational methods to analyze data and support decisions across drug discovery, preclinical research, clinical trials, manufacturing, and regulatory activities.
Artificial intelligence is now reshaping every stage of drug development, and companies are already using it to improve success rates and shorten timelines. Below, we'll take a look at some of the areas where AI is already having a major impact.
Applications of AI in drug discovery
AI in drug discovery helps scientists find new targets for medicines by scanning huge amounts of biomedical data, like genetic information and research papers. It spots patterns and connections that would be too complex or time-consuming for humans to detect on their own.
Deep learning and generative algorithms bring a faster way to discover new drugs. Instead of only searching through existing molecules, these systems can design brand-new ones from scratch, based on patterns that suggest what's most likely to be effective.
Machine learning models can also predict drug efficacy, toxicity, and optimal molecular structures, often reducing the discovery phase by one to two years, turning what used to be exhaustive manual screening into a narrower, evidence-led shortlist before a single lab experiment runs.
Recommended learning: AI innovations and challenges in the in the pharmaceutical Industry.
Applications of AI in drug development
As drug candidates are refined, AI simulates how molecules interact in the body, helping researchers quickly identify which compounds are most promising.
During preclinical testing, AI models predict how a drug will be absorbed, processed, and eliminated by the body, while flagging potential toxicities. That supports faster, risk-based decisions and can reduce the need for animal studies.
AI's influence is even more visible in clinical trials and manufacturing:
- Patient recruitment: AI analyzes real-world data to select suitable patients and flags underperforming sites early, before they cause costly delays.
- Adaptive trial design: AI-driven analytics let protocol studies adjust in real time based on interim data.
- Manufacturing: AI monitors production in real time, spotting quality deviations early, while predictive maintenance keeps equipment reliable.
The manufacturing and operations segment alone is projected to save the industry $4 to 7 billion annually, part of a wider $60 to 110 billion in yearly value McKinsey estimates generative AI could generate across the pharmaceutical industry. Real-world examples already show what that looks like in practice:
- BioNTech integrated AI at multiple stages of its COVID-19 vaccine development and pandemic response, using it to optimize mRNA vaccine design by analyzing viral genome sequences and predicting the most effective antigen targets.
- Insilico Medicine used AI to design a novel drug candidate for idiopathic pulmonary fibrosis (IPF), moving from initial concept to human clinical trials in under 30 months, a record pace for the industry.
- Exscientia and Sumitomo Dainippon Pharma advanced DSP-1181, the first fully AI-designed drug molecule, into a clinical trial within roughly 12 months of project initiation.
Benefits of AI in drug development
Integrating AI into the pharmaceutical pipeline speeds up processes, reduces risk, and improves decision-making from lab bench to production line. It doesn't just enhance individual tasks, it connects the dots across discovery and development and changes how medicines reach the market.
Let's break down the key benefits:
Benefits of integrating AI in drug discovery
Drug discovery is traditionally slow, expensive, and uncertain. AI changes that by speeding up how scientists identify, design, and test potential drug candidates, flagging the most promising starting points in days rather than months.
Generative AI goes further, designing new molecules tailored to hit specific targets and cutting down the trial-and-error phase. By predicting how these molecules will behave in the body, including efficacy, safety, and toxicity, AI can help teams make earlier, more informed choices and reduce costly failures later on.
Benefits of integrating AI into drug development
That same real-time, evidence-led approach keeps paying off once a candidate moves past discovery. Trials can run leaner because sites and patients are chosen with more evidence up front, and protocols can flex instead of locking in assumptions for years.
In manufacturing, AI can help teams surface signals earlier, prioritizing investigation, and reducing the manual effort it takes to monitor large volumes of process data. Quality teams still assess the findings and make the final decision, with AI helping them bring the relevant evidence together faster.
Regulatory support of AI initiatives for drug discovery and development
AI regulation in drug development is evolving quickly, with regulators in both the U.S. and EU moving from broad guidance toward more concrete expectations and legal requirements.
FDA guidance on AI in drug development
In January 2025, the U.S. Food and Drug Administration (FDA) issued a draft guidance titled Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. This guidance gives sponsors and other stakeholders recommendations for using AI-generated data to support regulatory decisions on the safety, effectiveness, or quality of drugs, and emphasizes assessing credibility based on risk.
There's also a publication called "Artificial Intelligence and Medical Products: How CBER, CDER, CDRH, and OCP are Working Together", released in March 2024 and updated in February 2025, which outlines how the FDA's medical product centers are working together to promote the safe and responsible use of AI.
EMA guidance on AI across the medicinal product lifecycle
Similarly, the European Medicines Agency (EMA) published a reflection paper in July 2023 on the use of AI in the medicinal product lifecycle. This document outlines the agency's current thinking on applying AI and ML technologies throughout the various stages of the medicinal product lifecycle, from drug discovery to post-authorization activities, while addressing risks such as lack of transparency and potential bias.
The EMA also published a Reflection Paper and launched a multi-year Big Data Workplan to integrate AI into the EU regulatory framework. Their message is clear: AI is welcome, but it must meet the same standards for safety, quality, and compliance as any other process or tool.
EU AI Act obligations for drug developers
While the FDA and EMA guidance describes what regulators expect from responsible AI use. The EU AI Act takes things a step further: it introduces binding legal requirements, with specific compliance deadlines for certain high-risk AI systems.
For drug developers, the first thing to get right is scope, and here the Act is narrower than many assume. Medicinal products legislation is not among the harmonization laws listed in Annex I, and drug development, manufacturing, quality control and pharmacovigilance are not among the Annex III use cases.
AI that drafts a validation protocol, optimizes a process step, predicts a dissolution profile or screens adverse-event reports is therefore not high-risk under the Act simply because the product it serves is a medicine. Those uses remain governed by GxP: EU GMP Annex 11, the forthcoming Annex 22, 21 CFR Part 11 and ICH guidance.
Where drug developers do fall within the high-risk rule is at the edges of the medicinal product: AI in a medical device or IVD, including software as a medical device, companion diagnostics and the device component of drug-device combination products (Annex I, via the MDR and IVDR), and AI used for decisions about your own workforce, such as recruitment or performance evaluation (Annex III).
Under the EU AI Act (Regulation (EU) 2024/1689), as amended by the Digital Omnibus on AI (Regulation (EU) 2026/1744), certain high-risk AI systems apply from 2 December 2027 for standalone high-risk systems covered by Annex III, or 2 August 2028 for AI embedded in regulated products covered by Annex I.
Two obligations already apply to every organization using AI, whatever the risk tier: the AI literacy duty in Article 4, in force since 2 February 2025, and the transparency duties in Article 50, in force since 2 August 2026.
That might make it sound like there's plenty of time, but preparing for compliance involves more than checking a box. Drug developers need to inventory the AI systems they use, determine which, if any, fall within the Act's scope, assess the risks involved, and establish the documentation, oversight, and evidence needed to demonstrate compliance, for the AI Act where it applies and for GxP everywhere else.
For a full walkthrough of scope, obligations, and how the EU AI Act interacts with existing GxP requirements, see our practical guide to EU AI Act compliance in life sciences.
Recommended learning: Upcoming AI regulations for pharma and medical devices.
What are the biggest challenges of using AI in drug development?
AI's biggest challenges in drug development are less about the algorithms and more about governance. That means organizations need more than technically capable models. They need the processes, evidence, controls, and human judgment to determine whether those outputs are appropriate for their intended use.
Three challenges are particularly important:
- Knowing when AI can be trusted. AI outputs can look convincing without being reliable. Teams need to understand the quality of the underlying data, the limitations of the model being used. Validation should therefore be tied to intended use and risk instead of being a one-time exercise.
- Keeping judgment with people. AI can identify patterns, surface anomalies, and recommend actions, but it should not replace a human decision-maker. The goal is not human oversight as a checkbox, but human judgment as an active part of the process.
- Building accountability around the technology. When an AI-supported decision goes wrong, organizations need to be able to reconstruct what happened: what data was used, what the system produced, what controls were applied, and who made the final decision. That requires governance and quality processes that create an evidence trail around AI use.
The question then for drug manufacturers is not simply whether an AI system meets a regulatory requirement. It is whether the organization has enough evidence, oversight, and accountability to exercise sound judgment when using it.
As AI becomes more embedded in drug development, quality teams have an opportunity to move beyond checking whether processes comply and help determine how AI can be used responsibly in the first place.
“With a drug product, we won’t truly know if there’s a problem until it’s on the market and used by millions. We hope there isn’t—clinical trials help—but the real test comes post-launch. By using advanced tools to forecast and detect patterns, we can predict issues and act proactively. That’s why integrating AI into our QMS is so important.”
Martin King, Regulatory Affairs & Quality Assurance expert
Conclusion
AI is already changing drug development, but its greatest value will lie in applying it where it can surface evidence earlier, reduce avoidable failures, and support better decisions as the stakes increase.
For pharma and biotech teams, the challenge now is to turn that potential into responsible, practical adoption. As regulatory expectations continue to evolve, successful AI adoption will depend not only on what the technology can do, but on the quality of the evidence behind it, the controls around it, and the human judgment guiding its use.
The goal is not simply faster drug development. It is better-informed development that can move promising treatments forward with greater confidence and, ultimately, get them to patients sooner.
If you are in the life sciences and serious about making a difference, the time to engage with AI is now.
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