Designing motif-specific scaffolds through equivariant neural networks, geometric deep learning, and diffusion probabilistic models.
Mar 31, 2023 - Mar 31, 2025
Motifs are active structural sites tasked with beneficial biological functions that can be employed to create new vaccines and enzymes. When designing proteins, a crucial endeavor is the creation of stable scaffolds to support motifs, thus allowing them to maintain their shapes and, consequently, their function. Unfortunately, current machine learning-based techniques are timeconsuming and limited to small proteins, restricting their usefulness. Specifically, the project seeks to streamline the design process by rapidly providing high-quality scaffolds to experimentalists while considerably reducing the time required for their creation. This rapidity is particularly important when swift actions are required for facing emerging and fast-evolving diseases. This will be achieved by employing recent advances in diffusion modelling, geometrical deep learning, and graph models and networks.
National Research Council Canada
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Ottawa, Ontario, CA K1N 6N5
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University of Ottawa
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Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.