Grants and Contributions

As part of Multinational Corporate Collaboration Program, the project “PACAAL” will focus on using established mouse models to evaluate 4 top candidate antibodies co-developed by Paradox and Eli Lilly for the treatment of kappa light chain amyloidosis.

The project will support development of intellectual property capacity within the firm.

Peptides are an increasingly effective and important drug modality that can serve to combine the advantages of small molecule drugs and large proteins. Yet, their discovery faces limitations in terms of cost and expertise, reducing access to drug discovery processes. Embracing in silico methods holds promise for revolutionizing biologic therapeutics discovery. However, high-quality training sets are rare, and training data must be generated specifically for each new drug target interaction. The goal of this project is to create a self-driving lab (SDL)-based discovery platform for peptides by combining droplet microfluidics, cell-free protein expression, and machine learning (ML)-guided design. This project will use split optical proteins, which are composed of two parts, a peptide and larger protein fragment, as well as insulin, as model peptides and targets for high-throughput screening. This platform will shift peptide discovery from manual to automated, accelerating the development of next-gen tools and drugs with broad applications in research and medicine.

AI Assist – AI Data project to develop the data infrastructure in order to garner better insights around the firms users and their behaviour. These insights will be used to scope and develop AI/Machine Learning applications within the firm’s software.

AI-enabled drug discovery has led to the rapid discovery of novel drug
candidates and sparked significant investment in specialized drug
companies in Canada and around the world. However, one important
limitation of these proprietary platforms is they require large training
datasets that are labor intensive to produce, and computationally expensive
to analyze. This project will develop a self-driving lab (SDL) to develop a
machine-learning (ML) algorithm that integrates experimental constraints
directly into generative models as well as an orchestrator tool to control
experiments performed and refine hypotheses tested.
This approach not only streamlines the discovery process but also
enhances the diversity and quality of candidates generated. The project
team anticipates accelerated discovery by significantly reducing the time
required to identify promising peptide drug candidates through automated,
intelligent experimental design. This will lead to innovative therapeutics, as
AI-driven hypothesis generation to discover peptides with unprecedented
therapeutic potential.

The EV Challenge is designed to promote and inspire high school students interested in robotics and electric vehicle to learn about and seek careers in electric vehicle industry.

To encourage scientific research and related scientific activities (e.g. data collection, integration, monitoring, and communication, including communication via conferences and symposia) and dissemination of scientific knowledge in the areas of: (a) Fisheries and Aquaculture Science; (b) Ecosystem Science; (c) Oceanography and Modelling; and (d) Freshwater Science.

The purpose of the Oceans Management Contribution funding is to support the development and implementation of oceans conservation and management activities in Canada.

(operating as L'nuey) in the continued development and implementation of the Pituamkek Conservation Program for the Pituamkek area of northwestern PEI. This project includes the development of a Guardian Framework that will outline the program options for a potential future Pituamkek National Park Reserve guardian program.

This contribution supports co-management objectives both under the Archipelago Management Board’s and other protected area working groups to adapt operations, policy guidance and management now that Haida title to lands and foreshore areas has been recognized by provincial and federal governments.