Artificial intelligence is helping scientists design potential medicines in ways that were once difficult to imagine, but the biggest challenge remains proving that they work in people.
Imagine a future in which scientists can find promising treatments for diseases faster, test fewer unsuitable compounds and explore medicines for conditions that have received little research attention.
Artificial intelligence (AI) could help bring that future closer, although it has yet to prove that it can reliably deliver safer, more effective medicines at scale.
Developing a new drug is a long, expensive process that involves identifying a disease mechanism, designing a potential treatment, testing it in laboratories and animals, and conducting clinical trials in people. Many promising candidates fail because they prove ineffective or cause unacceptable side effects.
AI offers a new way to approach the earliest stages of this process.
Instead of relying entirely on researchers to identify and test potential chemical compounds one by one, AI systems can analyse vast amounts of biological and chemical data, predict how molecules might behave and suggest candidates worth investigating.
Think of it as a research assistant capable of searching through an enormous library of possibilities, although its predictions must still be checked in the real world.
A promising breakthrough in lung disease
One important example emerged in June 2025, when researchers published results from a clinical trial of rentosertib, an experimental drug developed using generative AI to treat idiopathic pulmonary fibrosis, a progressive lung disease that causes scarring and makes breathing increasingly difficult.
The study, published in “Nature Medicine”, involved 71 patients and tested the drug against a placebo over 12 weeks.
Patients receiving the highest once-daily dose showed an average improvement of 98.4 millilitres in forced vital capacity, a measure of how much air a person can exhale, while the placebo group recorded an average decline of 20.3 millilitres.
The results were encouraging, but the trial was small and relatively short, and the researchers said larger studies were needed to establish whether the treatment was effective and safe over longer periods. The drug is experimental, not an approved treatment.
What does the latest research tell us?
A study published online in October 2026 identified 129 AI-enabled drug candidates that had entered human clinical trials by March 2026, spanning 69 companies.
However, only five candidates had reached or completed phase three trials, the later stage of testing that typically involves larger groups of patients.
The findings suggest that AI is helping generate potential medicines, but there is still limited evidence that these candidates can consistently survive the later stages of drug development.
A separate review published in Nature Reviews Drug Discovery in August 2026 reached a similarly cautious conclusion, arguing that evidence of AI’s impact on clinically meaningful outcomes remains limited.
The real test is still ahead
AI could eventually help researchers identify new treatments for cancer, rare diseases and conditions for which existing medicines offer limited relief.
But predicting how a molecule might behave is not the same as proving that it will help a patient. Human biology is complex, and a drug that looks promising on a computer can fail when tested in the body.
There are also questions about whether faster discovery will translate into lower medicine prices or better access for patients.
For now, AI is best understood as a powerful new tool for scientists, rather than a replacement for them.
The breakthrough worth watching is not simply whether AI can design a drug, but whether it can help deliver medicines that are demonstrably safer, more effective and accessible to the people who need them.
Reference:
- Xu et al., A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial, Nature Medicine, 3 June 2025. Read the study.
- Artificial intelligence-enabled drug discovery: A growing but largely early-stage clinical pipeline, Cancer Treatment Reviews, published online 8 October 2026. Read the research.
- Bender et al., Artificial intelligence in drug discovery: what it is, where we stand and the path forward, Nature Reviews Drug Discovery, 7 August 2026. Read the review.
- Reuters, US government, Google join Zuckerberg-backed Biohub in $1.8 billion push for AI biology data, 7 October 2026. Read the report.