Automated self-driving labs accelerate the search for new medicines

The Acceleration Consortium (AC) at the University of Toronto and the Structural Genomics Consortium (SGC) announced today a formalized partnership to help tackle a persistent challenge in biomedicine and early drug discovery: the development of bioactive molecules with drug-like properties, advancing our understanding of human health and disease and jump-starting new drug discovery programs.

The Acceleration Consortium is driving a revolutionary era for scientific discovery, and this partnership will be a force multiplier for Canadian-led innovation in pharmaceutical sciences. Combining AC's global self-driving lab expertise with SGC's open science leadership expedites the search, design, and testing of new drugs, helping patients access potentially life-saving or more effective treatments faster."

Alán Aspuru-Guzik, director of the AC and professor in the departments of chemistry and computer science, University of Toronto

Recent advances in artificial intelligence are poised to dramatically increase the discovery of millions upon millions of chemical compounds that may have therapeutic potential. However, the current challenge is to rapidly test and further develop these promising molecules into potent, selective and pharmacologically relevant agents, and then onwards to new drugs.

This growing gap between the speed of initial compound identification and the capacity to optimize them has emerged as a critical barrier to scaling early drug discovery – a traditionally slow and resource-intensive process.

To break down this barrier, SGC's partnership with the AC invests in a new approach to medicinal chemistry (the science of designing new drugs), integrating self-driving lab (SDL) capabilities into it research efforts.

The Medicinal Chemistry SDL is part of the AC, a flagship University of Toronto initiative. The SDL combines AI, robotics, and advanced computing to automate iterative Design–Make–Test–Analyze cycles, enabling rapid synthesis, testing, and refinement of potential drug compounds.

The AC-SGC collaboration will play a central role in advancing SGC's Target 2035 initiative, which aims to develop a pharmacological tool for every human protein. Achieving this goal requires not only the generation of large-scale, high-quality protein–ligand datasets by SGC, but also the AC SDL's ability to efficiently optimize chemical starting points into usable pharmacologically active tools with AI and automation.

"As Target 2035 progresses, we anticipate a rapid increase in validated chemical starting points – but current medicinal chemistry workflows are not equipped to process these at scale," said Cheryl Arrowsmith, Chief Scientist of SGC-Toronto Laboratory and co-advisor of the Medicinal Chemistry SDL, alongside Robert Batey. Both are professors at the University of Toronto in the departments of medical biophysics and chemistry, respectively. "This is why SGC is making a strategic investment in self-driving lab capabilities, as one of our many approaches to enable the next phase of scalable, AI-driven drug discovery."

The AC SDL will operate under SGC's open science model, ensuring that methods, data, and results are made openly available to the global research community. It will also be embedded within the broader ecosystems of the Collaborative Centre for Drug Discovery at the University Health Network and the SGC partners focused on advancing AI-driven drug discovery, supported by approximately $50 million in industry funding.