AI for design of multi-targeted therapeutics

Multi-targeted therapeutics, including antibody-based and immune cell-based therapeutics, are relatively recent but very promising and fast-growing technologies. The goal of this project will be to develop a computational solution for data-driven design of multi-targeted therapeutics for various indications including cancer and inflammatory conditions. The platform will use human input and machine learning to extract patterns from big data (biomedical text and omics data) to generate single and multi-target associations (with diseases, clinical-stage therapeutics, normal tissues, immune cells, etc.) for high-precision targeting with antibody and cell-based therapeutics. Novel target combinations will be candidates for experimental validation. The NRC will apply its expertise in data mining, deep learning and experimental validation.

Project team

Dr. François Fauteux

Dr. Fauteux is a research officer with the National Research Council. He is an expert in omics data mining and machine learning and his research interest lies in drug discovery and crop improvement.