The University of Toronto’s Acceleration Consortium (AC) and Structural Genomics Consortium (SGC) today announced a formal partnership to help address persistent challenges in biomedicine and early drug discovery: developing bioactive molecules with drug-like properties, advancing understanding of human health and disease, and revitalizing drug discovery programs.
The Acceleration Consortium is driving an innovative era of scientific discovery, and this partnership will double the power of Canadian-led pharmaceutical innovation. Combining AC’s global self-driving lab expertise with SGC’s open science leadership will speed the discovery, design and testing of new medicines, giving patients faster access to potentially life-saving and more effective treatments. ”
Alan Aspuru-Guzik, AC Director, Professor, Department of Chemistry and Computer Science, University of Toronto
Recent advances in artificial intelligence are poised to dramatically increase the discovery of millions of compounds with therapeutic potential. However, the current challenge is to rapidly test and further develop these promising molecules into potent, selective and pharmacologically relevant drugs, and even new drugs.
This growing gap between the speed of initial compound identification and the ability to optimize it has emerged as a significant barrier to scaling up early drug discovery, which is traditionally a time-consuming and resource-intensive process.
To break down this barrier, the SGC and AC partnership is investing in new approaches to medicinal chemistry (the science of new drug design) and integrating the capabilities of the Self-Driving Laboratory (SDL) into its research efforts.
Medicinal Chemistry SDL is part of the University of Toronto’s flagship initiative, AC. SDL combines AI, robotics, and advanced computing to automate iterative cycles of design, manufacture, test, and analysis, enabling the rapid synthesis, testing, and purification of potential drug compounds.
The AC-SGC collaboration will play a central role in advancing the SGC’s Target 2035 initiative, which aims to develop pharmacological tools for the full range of human proteins. Achieving this goal requires not only the generation of large-scale, high-quality protein-ligand datasets by SGC, but also the ability of AC SDL to efficiently optimize chemical starting points into usable pharmacologically active tools using AI and automation.
“As Target 2035 progresses, we expect to see a rapid increase in the number of validated chemical starting points, but current medicinal chemistry workflows do not have the capacity to process these at scale,” said Cheryl Arrowsmith, principal investigator at the SGC Toronto Institute and co-advisor of the Medicinal Chemistry SDL, along with Robert Battey. They are professors of medical biophysics and chemistry, respectively, at the University of Toronto. “This is why SGC is making strategic investments in self-driving lab capabilities as one of many approaches to enable the next stage of scalable, AI-driven drug discovery.”
AC SDL operates under SGC’s open science model, making methods, data, and results openly available to the global research community. It will also be embedded within the University Medical Network’s Collaborative Center for Drug Discovery and a broader ecosystem of SGC partners focused on advancing AI-driven drug discovery with the support of approximately $50 million in industry funding.
