The new possibilities of AI are, however, trying to shake up an environment that is moving forward with a very rigid inertia that is difficult to redirect. Pharmaceuticals and private investors operate far away from the development of new antibiotics and innovation has slowed down over the last four decades. Since the 1980s, no new family of antibiotics has been marketed, despite the fact that available compounds have been steadily losing efficacy due to antimicrobial resistant bacteria (AMR).
By mid-2025, only 32 antimicrobial drugs were in clinical development, and of these, only 12 could be considered innovative according to the WHO. The fate for most of them will be failure in clinical trials and lack of regulatory approval.
Private investment in antimicrobial development is scarce because of the dysfunctionality of the market itself, which has meant that companies that have secured permits for new compounds in recent years have had to declare bankruptcy almost immediately. In fact, small companies account for 80% of proposed new antibiotic therapies, while 8% emerge from non-profit institutes and universities, and only 12% originate from large companies.
Each formulation requires on average around $1 billion in development, and recouping that investment is extremely difficult for a class of drugs that, unlike those that treat lifelong chronic diseases, are used for a short period of time - the duration of the infection. As a result, it could happen that AI generates a large number of promising therapeutic candidates, but there is no funding necessary for them to complete clinical trials and reach patients.
Antimicrobial resistance is projected to cause up to 1.91 million attributable deaths and 8.22 million associated deaths by 2050. Without further interventions, it will be unlikely to achieve the WHO's proposed 10% reduction in AMR mortality by 2030 and meet the requirements of the European Parliament, which in 2023 recognised AMR as one of the top three priority health threats in the EU.
On the technology side, data quality, model interpretability and real-world implementation of the results obtained from the computation remain the big challenges ahead. And it is not strictly a question of quantity: large datasets can help train advanced modelling, but smaller, well-annotated and accurate datasets can provide more valuable information by avoiding excessive noise. International collaboration is needed to share information and knowledge between institutions.
As a way to facilitate a strategic and integrative vision, some experts advocate a multidisciplinary approach that integrates AI with other emerging technologies such as synthetic biology and nanomedicine to preserve the efficacy of antibiotics in the future.