The Acceleration Consortium at the University of Toronto and the Structural Genomics Consortium announced on June 10 a formalized partnership aimed at addressing challenges in biomedicine and early drug discovery. The collaboration seeks to develop bioactive molecules with drug-like properties, enhance understanding of human health and disease, and support new drug discovery programs.
Advances in artificial intelligence have increased the identification of chemical compounds with potential therapeutic benefits. However, rapidly testing and developing these molecules into effective drugs remains a challenge. This gap between initial compound identification and optimization has become a significant barrier to scaling early-stage drug discovery.
To address this issue, the Structural Genomics Consortium's partnership with the Acceleration Consortium will invest in integrating self-driving lab capabilities into medicinal chemistry research efforts. The Medicinal Chemistry Self-Driving Lab is part of the Acceleration Consortium initiative at the University of Toronto. It combines artificial intelligence, robotics, and advanced computing to automate iterative design–make–test–analyze cycles for rapid synthesis, testing, and refinement of potential drug compounds.
The collaboration will support SGC's Target 2035 initiative, which aims to develop a pharmacological tool for every human protein. This effort relies on generating large-scale protein–ligand datasets by SGC as well as optimizing chemical starting points using AI-driven automation provided by the self-driving lab.
"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. "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 Medicinal Chemistry Self-Driving Lab will operate under an open science model that ensures methods, data, and results are available globally. It will also be integrated within broader research ecosystems focused on advancing AI-driven drug discovery at affiliated institutions.
