Lessons Learned for AI in Small Molecule Drug Discovery

Despite the buzz around artificial intelligence (AI), most industry insiders know that the use of machine learning (ML) in drug discovery is nothing new. For more than a decade, researchers have used computational techniques for many purposes, such as finding hits, modeling drug-protein interactions, and predicting reaction rates.

What is new is the hype. As AI has taken off in other industries, countless start-ups have emerged promising to transform drug discovery and design with AI-based technologies. While a few “AI-native” candidates are in clinical trials, around 90% remain in discovery or preclinical development, so it will take years to see if the bets pay off. This begs the question: Is AI for drug discovery more hype than hope? Absolutely not. Do we need to adjust our expectations and position for success? Absolutely, yes.

In this webinar, we discuss:

  • Learnings from previous technology implementation challenges

  • Learnings from adjacent industries

  • Three keys to successful AI implementation

Speakers and Panelists

Haydn Boehm

Haydn is focused on helping the life science and drug discovery industries learn how to harmonize their science and data, and accelerate innovation to make the world a healthier, cleaner, better place to live. Haydn has 20 years sales and marketing leadership experience in the life science industry working with companies such as ThermoFisher Scientific, Johnson Matthey and more recently with Merck KGaA’s Connected Lab Program. Haydn has a PhD in Organic Chemistry from the University of Nottingham, UK., and lives on the Jurassic Coast in Devon, UK with his family.