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Antimicrobial discovery

Antibiotics are one of the most important breakthroughs of modern medicine. However, antimicrobial drug resistance (AMR) in bacteria renders many antibiotics ineffective against previously treatable infections, contributing to more than 5 million deaths/year worldwide.

To overcome the shadow pandemic of bacterial AMR, we need new antibiotics with novel mechanisms of action. However, antibiotic drug discovery has stagnated over the past several decades. Key difficulties in the field include the diversity and complexity of bacterial pathogens and their responses to different treatments, inadequate exploration of potentially effective antibiotic designs in existing drug-discovery pipelines and high dropout rates of antibiotic candidates due to toxicity or lack of effectiveness in clinical trials.

In this project, we propose to overcome these challenges using the latest innovations in AI, microbiology, and drug discovery. Specifically, our vision is to build AI-based generative drug design model(s) that analyze complex chemical, biological and toxicological screening data to generate novel drug design predictions that optimize the multiple objectives of a good antibiotic.

 

Aims of the "intelligent discovery of antibiotics" project - schematic

This collaborative vision brings together 15 world-leading research teams from across Canada’s academic, public and private sectors. Our specific aims in this project are:

Aim 1: To expand antibiotic design into unexplored and underexploited reaches of “chemical space”

Aim 2: To train and test the generative antibiotic design model(s) using experimental screens of bacterial activity

Aim 3: To generate structural predictions of target binding to identify and optimize antibiotic leads

Aim 4: To optimize antibiotic leads through metabolomic profiling of toxicity, efficacy, and mechanism of action

Aim 5: To develop fast and scalable synthetic routes for antibiotics and their building blocks

Here, you can learn more about the TEAM.

Funding: The Canada Biomedical Research Fund and/or Biosciences Research Infrastructure Fund (grant CBRF2-2023-00107)