Three Key Considerations When Implementing AI in Drug Discovery

October 6, 2022
8:00 a.m. PDT, 11:00 a.m. EDT, 16:00 BST, 17:00 CEST
Duration: 60 Minutes


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 will discuss Three Keys to Implementing AI in Drug Discovery Implementing AI in drug discovery requires: reasonable expectations, clean data, and collaboration.

Key Learning Objectives:
  • Reasonable expectations of AI 
  • Readiness for AI 
Who Should Attend:
  • Cheminformatics leaders  
  • Cheminformatics scientists 
  • Drug discovery leaders  
  • Drug discovery scientists 

Brought to you by:
Dotmatics Logo


Haydn Boehm
Director of Product Marketing
Melissa O'Meara
Forensic Science Consultant
C&EN Media Group


*By submitting this form, you agree to receive more information on related products and services from the American Chemical Society (ACS Publications) and its sponsor via email. ACS takes your privacy seriously. For more information, please see the ACS Privacy Policy.

© 2024 American Chemical Society, 1155 16th St NW, Washington, DC 20036, USA. View our Privacy Policy