Perspectives

AI in Drug Discovery

How is AI technology helping in the response to COVID-19?

Join Karen Taylor and special guests, as we explore the potential for Artificial Intelligence (AI) in accelerating biopharma research and development and how it is being deployed to combat COVID-19.

Since the emergence of COVID-19, the biopharma industry has been working tirelessly to develop both preventive and therapeutic interventions. As the Life Sciences industry pivots to meet this challenge, an increasing number of academic and industry groups are turning to Artificial Intelligence (AI) tools as a means to identify more precise targeted treatments to fight the COVID-19 pandemic. 

In this two part discussion, Karen Taylor, Director of Deloitte Centre for Health Solutions, explores the use of AI technology in the fight to combat COVID-19.

Karen is joined by panellists Margaretta Colangelo and Dmitry Kaminskiy, co-founders and managing partners of Deep Knowledge Ventures, a data-driven investment fund focused on DeepTech and publications that include quarterly tracking of the evolution of AI for drug discovery companies and showcase breakthrough discoveries. They are joined by Alex Zhavoronkov an expert in AI for drug discovery and aging research. Alex is the founder and CEO of Insilico Medicine, an AI for drug discovery start-up that has over 200 research collaborations worldwide.

This discussion draws on the findings from our recent report: Intelligent Drug Discovery which examines how AI-enabled solutions are transforming the drug discovery process and enabling the development of more precise targeted treatments.

The drug discovery landscape

In the first part of our panel discussion, we focus on the increasing use of Artificial Intelligence tools in drug discovery and the types of treatments AI companies are focusing on.

The role of AI in the response to COVID-19

In part two, our panel discusses how Artificial Intelligence is aiding the response to the COVID-19 pandemic and describe what the post-COVID world might look like.

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