The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Machine Learning at GSK with Kim Branson - #536

Nov 15, 2021

Kim Branson, SVP and global head of AI and machine learning at GSK, shares insights on leveraging machine learning in pharmaceuticals. He discusses the integration of genetics data for drug discovery and the impressive 500 billion node knowledge graph designed to mine scientific literature. Kim also highlights their recent collaboration with King’s College, focusing on personalized cancer research using AI. The conversation dives into the challenges of building scalable AI infrastructures and the critical need for robust evaluation programs in real-world applications.

01:00:36

Creator website

Episode guests

Kim Branson

AI Chapters

Episode notes

Intro

2min

AI in Pharma: A Transformative Journey

9min

The Complexity of Genetic Variants and Protein Functionality

2min

AI in Drug Discovery and Genomics

22min

Building Scalable AI Infrastructure and Computational Partnerships

3min

Understanding Featurization in Machine Learning for Genetic Analysis

2min

Navigating Machine Learning in Healthcare

21min

Today we’re joined by Kim Branson, the SVP and global head of artificial intelligence and machine learning at GSK.

We cover a lot of ground in our conversation, starting with a breakdown of GSK’s core pharmaceutical business, and how ML/AI fits into that equation, use cases that appear using genetics data as a data source, including sequential learning for drug discovery. We also explore the 500 billion node knowledge graph Kim’s team built to mine scientific literature, and their “AI Hub”, the ML/AI infrastructure team that handles all tooling and engineering problems within their organization. Finally, we explore their recent cancer research collaboration with King’s College, which is tasked with understanding the individualized needs of high- and low-risk cancer patients using ML/AI amongst other technologies.

The complete show notes for this episode can be found at twimlai.com/go/536.

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