Conferences  >  Medicine & Healthcare  >  Pharmacology and Drug Development  >  Canada

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

The vaccine development pipeline is a time, cost and labour-intensive process. Mathematical and computational methods can alleviate challenges related to this intensive process as well as providing further insights into the efficacy and safety of vaccines. These methods are key components of model-informed vaccine development (MIVD) and quantitative systems pharmacology/toxicology (QSP/T). MIVD and QSP/T can be used in tandem to better understand the biological processes and outcomes from vaccination administration, spurring future research. This workshop will strengthen and contribute to the ongoing development of this interdisciplinary community of biologists, mathematicians and vaccinologists.

Applied Mathematics (in general),    Modeling and Simulation

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

The landscape of drug design is rapidly evolving, with computational methods and artificial intelligence (AI) playing an increasingly important role. This workshop aims to explore cutting-edge mathematical approaches that are transforming the field of computational drug design. We will delve into multiscale modeling techniques that bridge the gap between molecular and macroscopic levels, providing a comprehensive understanding of drug interactions and dynamics. Additionally, we will highlight the integration of generative AI and machine learning (ML) algorithms in predicting drug efficacy, optimizing molecular structure, speeding up drug repurposing, understanding potential toxicity, and thus accelerating the drug discovery and development process. By bringing together diverse experts from academia, industry, and innovation support organizations, and adding early career researchers to the mix, this workshop will foster interdisciplinary collaboration and innovation. Participants will gain insights into the latest developments, discuss challenges, and explore future opportunities in the quest for more effective and efficient drug design.

Applied Mathematics (in general),    Neural Networks and Artificial Intelligence, Machine Learning