---
title: Project | PandemicStop-AI
description: "PandemicStop-AI: Rapidly discover new antibiotics by implementing a continuous learning loop powered by artificial intelligence and high-throughput data generation on candidate molecules."
---

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# Funded Projects

# PandemicStop-AI

## Rapidly discover new antibiotics by implementing a continuous learning loop powered by artificial intelligence and high-throughput data generation on candidate molecules.

![pandemicstop ailogo-dark](https://pppec.ca/hubfs/pandemicstop%20ailogo-dark.svg)

 

![AI](https://pppec.ca/hs-fs/hubfs/AI.png?width=600&height=441&name=AI.png "AI")

---

# Team and Partners

## Scientific Director

![Yves Brun](https://pppec.ca/hs-fs/hubfs/Yves%20Brun.jpg?width=256&height=154&name=Yves%20Brun.jpg)

 

Yves Brun  
Université de Montréal

 

 **Co-Director**  
![CORBEIL-Jacques\_2025-556x556px](https://pppec.ca/hs-fs/hubfs/CORBEIL-Jacques_2025-556x556px.png?width=156&height=156&name=CORBEIL-Jacques_2025-556x556px.png)

 

Jacques Corbeil  
Université Laval  and Mila 

 

## Scientific team

- Anne Marinier – Université de Montréal and IRIC
- Dominique Beaini — Valence Labs, Université de Montréal and Mila
- Sarath Chandar — Polytechnique Montréal and Mila
- André Charette — Université de Montréal
- Audrey Durand — Université Laval and Mila
- Alex Hernandez-Garcia — Université de Montréal and Mila
- Flavie Lavoie-Cardinal — Université Laval and Mila
- Michal Koziarski — The Hospital for Sick Children / University of Toronto
- Dao Nguyen — McGill University

- Jian Tang — HEC Montréal and Mila
- Pierre Thibault — Université de Montréal
- Michael Tyers — University of Toronto / The Hospital for Sick Children
- Sébastien Lemieux — Université de Montréal and Mila
- David Wishart — University of Alberta
- Frédérique Le Roux — Université de Montréal
- Marilyse Duperthuis — Université de Montréal
- Soren Gantt — Université de Montréal

 

## Institutions and Key Partners

![Pandemicstop logos](https://pppec.ca/hs-fs/hubfs/Pandemicstop%20logos.png?width=1280&height=379&name=Pandemicstop%20logos.png)

Beyond its core consortium, PandemicStop-AI is supported by a broader ecosystem of national and international partners that strengthens its innovation capacity and accelerates the translation of discoveries into real-world applications. This ecosystem includes the McGill Antimicrobial Resistance Centre, the AMRQ initiative, the pan-Canadian Can-AMR-Net network, Médicament Québec, the Institut Pasteur, the Canada Biomedical Research Fund (CBRF), ECaPPH, and IVADO R3AI, which contribute to scientific collaboration, knowledge sharing, talent development, and the project’s visibility.

The consortium is also continuing to expand this strategic network internationally, including through collaborations with Gr-ADI, NCCR AntiResist, CO-ADD, and Breakthrough, with the aim of accelerating the discovery of new antibiotics and strengthening preparedness for future bacterial pandemics.

 

---

            ![Défi](https://pppec.ca/hs-fs/hubfs/D%C3%A9fi.png?width=130&height=126&name=D%C3%A9fi.png) 

**The Challenge**

Antimicrobial resistance is one of the greatest threats to global health. In 2019, it caused more deaths than COVID-19 did during the first two years of the pandemic.

Despite this urgency, very few new classes of antibiotics have been discovered in recent decades. The discovery of new treatments remains a long, costly, and complex process, while the emergence of resistant bacteria could lead to future pandemics.

Canada must therefore have the capacity to rapidly discover new antibiotics when a new pathogen emerges.

 

![solution](https://pppec.ca/hs-fs/hubfs/solution.png?width=130&height=125&name=solution.png) 

The solution

PandemicStop-AI is developing an integrated platform that combines artificial intelligence, high-throughput screening technologies, and the expertise of an extensive network of partners to accelerate the discovery of new classes of antibiotics.

By leveraging large volumes of experimental data, the platform can explore the vast chemical space of potential molecules much more rapidly, identify the most promising candidates, and advance them toward preclinical testing.

The goal is to equip Canada with a rapid-response capability for future bacterial outbreaks.

 

![Fonctionnement](https://pppec.ca/hs-fs/hubfs/Fonctionnement.png?width=130&height=127&name=Fonctionnement.png)

 How it workds

1\. Learn: Generate large datasets on interactions between bacteria and millions of chemical compounds to train artificial intelligence models.

2\. Discover: Use artificial intelligence and automated screening platforms to rapidly identify new antibiotics that are both effective and safe.

3\. Accelerate: Optimize the most promising candidates, develop their manufacturing in Canada, and prepare them for preclinical and clinical studies.

---

 Expected Impact 

 PandemicStop-AI  will allow :

- Accelerate the discovery of new classes of antibiotics;
- Strengthen Canada’s capacity to respond rapidly to the emergence of resistant bacteria;
- Reduce reliance on international supply chains for critical molecules;
- Support the development of Canadian expertise in artificial intelligence applied to drug discovery;
- Better protect the population against future bacterial outbreaks.

![anitbiotics](https://pppec.ca/hs-fs/hubfs/anitbiotics.png?width=500&height=331&name=anitbiotics.png "anitbiotics")

![réseau](https://pppec.ca/hs-fs/hubfs/r%C3%A9seau.png?width=500&height=381&name=r%C3%A9seau.png "réseau")

Leveraging Impact 

The project brings together a unique pan-Canadian ecosystem of academic, public-sector, and industry partners, spanning the entire antibiotic discovery and development value chain.

By integrating artificial intelligence, screening platforms, medicinal chemistry, and manufacturing, PandemicStop-AI is building a sustainable capacity that will also accelerate future collaborative projects and therapeutic innovation in medicinal chemistry.

 

Infrastructures Established

 A national platform for accelerated antibiotic discovery that includes: 

- Artificial intelligence models trained to identify new therapeutic candidates;
- Automated high-throughput screening platforms capable of analyzing thousands of compounds per day;
- Advanced cellular imaging technologies to rapidly characterize the mechanisms of action of candidate molecules;
- Capabilities for the synthesis and scale-up of the most promising candidates;
- An integrated network connecting discovery, development, and preparation for preclinical studies.

 

![anitbiotics2-1](https://pppec.ca/hs-fs/hubfs/anitbiotics2-1.png?width=500&height=335&name=anitbiotics2-1.png "anitbiotics2-1")

---

**Project Status**   
Development and Validation of Accelerated Antibiotic Discovery Platforms

![jalons](https://pppec.ca/hs-fs/hubfs/jalons.png?width=128&height=106&name=jalons.png) Milestones Acheived

- Establishment of a pan-Canadian consortium bringing together expertise in microbiology, artificial intelligence, chemistry, and pharmaceutical development.
- Development of automated screening platforms and artificial intelligence models.
- Identification of initial promising antibiotic candidates using experimental and *in silico* approaches.
- Validation of new prototype molecules showing activity against resistant bacteria.

![Next](https://pppec.ca/hs-fs/hubfs/Next.png?width=150&height=135&name=Next.png) Next Steps

- Optimize artificial intelligence models to further accelerate the discovery of new molecules.
- Advance the most promising candidates toward preclinical development.
- Deploy a platform that can be rapidly adapted in response to the emergence of a new resistant pathogen.

---

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