Regina Barzilay and Jim Collins win Pritzker Prize for AI and Science Research Excellence for work in AI-enabled antibiotic discovery

# Regina Barzilay and Jim Collins win Pritzker Prize for AI and Science Research Excellence for work in AI-enabled antibiotic discovery Source: [https://news.mit.edu/2026/regina-barzilay-jim-collins-win-pritzker-prize-ai-and-science-research-excellence-1102](https://news.mit.edu/2026/regina-barzilay-jim-collins-win-pritzker-prize-ai-and-science-research-excellence-1102) Regina Barzilay, MIT’s School of Engineering Distinguished Professor of AI and Health, and James Collins, MIT’s Termeer Professor of Medical Engineering and Science, are the recipients of the[2026 Pritzker Prize for AI in Science Research Excellence](https://datascience.uchicago.edu/news/the-university-of-chicago-announces-2026-recipients-of-the-pritzker-prize-for-ai-in-science-research-excellence/)\. The award recognizes their joint work in “pioneering AI\-enabled antibiotic discovery, integrating deep learning, bioengineering, and experimental validation to identify novel antibacterial candidates against drug\-resistant pathogens\.” Established in 2024 by the University of Chicago in partnership with Caltech, the Pritzker Prize for AI in Science Research Excellence recognizes contributions that jointly advance AI and the natural sciences or engineering\. Barzilay and Collins, who are faculty co\-leads of the[Abdul Latif Jameel Clinic for Machine Learning in Health](https://jclinic.mit.edu/)\(Jameel Clinic\) and affiliates of the MIT Schwarzman College of Computing and the MIT School of Engineering, published their first[breakthrough paper](https://www.cell.com/cell/fulltext/S0092-8674(20)30102-1)together in 2020 with then\-MIT postdoc and first author Jonathan Stokes \(who worked in the Collins lab from 2017 to 2021\) in*Cell\.*The paper described the first artificial intelligence\-discovered antibiotic, called[halicin](https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220), named for the fictional AI system HAL 9000 in Stanley Kubrick’s 1968 film*2001: A Space Odyssey*\. A broad\-spectrum antibiotic that was originally studied as a potential diabetes drug, halicin was identified by a deep learning model trained on 2,500 molecules\. Upon testing, the research team found that halicin was able to kill antibiotic\-resistant bacteria \(i\.e\., “superbugs”\) such as*Clostridium difficile*,*Acinetobacter baumannii*, and*Mycobacterium tuberculosis*\. A discovery process that once took years was completed in just a few weeks\. Amidst the growing alarm around the global antimicrobial resistance crisis, the results demonstrated that AI could prove to be an effective tool for rapidly identifying new and effective antibiotics\. Following halicin, Barzilay and Collins worked with Stokes \(now an assistant professor at McMaster University\) to use machine learning to identify[abaucin](https://news.mit.edu/2023/using-ai-scientists-combat-drug-resistant-infections-0525)in 2023, a narrow\-spectrum antibiotic demonstrating effectiveness against*A\. baumannii*, an antibiotic\-resistant bacteria commonly found on hospital doorknobs and in the wounds of U\.S\. soldiers stationed in Iraq and Afghanistan\. Notably, abaucin’s “narrow spectrum” efficacy against a single species of treatment\-resistant bacteria is its main advantage, minimizing the likelihood of bacteria developing resistance and harming beneficial bacteria in the gut\. “Historically, antibiotic discovery has depended on laborious, trial\-and\-error strategies,” says Collins\. “This process is slow, costly, and unpredictable, often yielding few viable antibiotic candidates\. With generative AI, we're now seeing a shift from AI being used as a tool for discovery to AI being used as a design tool\. This shift opens new frontiers in antibiotic discovery that could bring us the next generation of lifesaving drugs\.” In Barzilay’s view, “despite significant progress in molecular modeling, we are still in early stages of drug discovery\. With AI, we can only automate parts of the pipeline, rather than totally transforming the discovery process\. To make it happen, we need to work really hard to build realistic AI models of human biology\.”