Leukemia accounts for almost a third of all cancer cases diagnosed in children in Canada. While progress has been made in treating the majority of these cases, acute myeloid leukemia (AML) remains an exception, with surprisingly poor results. In order to identify new and more effective drugs to treat this disease, we have tested thousands of different molecules to determine which ones are capable of blocking the growth of leukemia cells both in patients and in the model leukemias we have created in our laboratory. Although this work has allowed us to discover potential new treatments, we were still able to test only 12,000 molecules instead of the tens of billions that could theoretically be created. In this project, we intend to overcome this limitation by employing powerful machine learning algorithms using our data to train a computer system to predict new molecules that can block the growth of leukemia cells. This research, which is carried out in partnership with Valencia Discovery, will help us to greatly increase the chances of finding new therapeutic molecules that can then be optimized. This will ultimately lead to improved treatments for young leukemia patients who currently need better drugs.
Lead Genome Centre: Génome Québec
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| Sébastien | Giguère | InVivo AI |