Decoding De Novo Design with Generative Methods
WEBINAR
Join us for an insightful webinar on "Decoding De Novo Design with Generative Methods," where we will explore cutting-edge applications of AI/ML to novel molecule design. This session will provide an overview of various de novo methods, including standard, scaffold-, reaction-, and structure-based approaches. We will delve into their applications across different workflow challenges such as hit identification, hit-to-lead (H2L), and lead optimization.
Learn about the specific use cases for each de novo method and understand the unique benefits they offer in designing novel, drug-like, and synthetically viable compounds. This webinar is designed to equip medicinal chemists and drug discovery scientists with the knowledge to unlock new molecule design possibilities and accelerate their drug development journey.
Seize this chance to deepen your understanding of generative AI-driven drug discovery and explore how AIDDISON™ software can augment your research methodologies.
Key topics discussed in this webinar include:
- Understand De Novo Drug Design: Gain insights into different de novo methods, including scaffold-, reaction-, and structure-based.
- Practical Applications: Explore how to apply de novo methods in hit identification, H2L, and lead optimization.
- Benefits & Advantages: Learn the unique benefits of each method for designing drug-like, synthetically viable compounds.
Who should attend:
Ideal for medicinal chemists and drug discovery scientists seeking to accelerate drug development with cutting-edge AI tools.
Speakers
Suhasini M Iyengar, PhD
AI and Cheminformatics Merck
Application and Discovery Scientist
Suha holds a Ph.D. in Computational Chemistry from Northeastern University, specializing in structure-based drug discovery for neurological disorders like Parkinson's and Alzheimer's disease. With expertise in utilizing cutting-edge computational tools for drug discovery, Suha has spearheaded projects in crafting novel inhibitors for critical protein targets associated with SARS-CoV-2. Her research portfolio boasts significant contributions to applying these innovative computational tools to modern drug discovery methodologies, alongside mentoring both undergraduate and graduate students in their own drug discovery pursuits. As an application scientist for the AI software AIDDISON™, Suha plays a vital role in driving customer interactions and acting as a liaison between the development team and end-users. Her deep expertise ensures that AIDDISON™ stays at the cutting edge of AI-driven drug discovery, providing exceptional value and innovative solutions to meet the needs of its users.
Peter Toogood, PhD
College of Pharmacy, University of Michigan
Research Associate Professor and Director of Michigan Drug Discovery (MDD)
Peter Toogood has pursued drug discovery in academia and in the private sector. Prior to joining U-M, he led medicinal chemistry teams at Parke-Davis, Pfizer and Lycera. He is a co-inventor of the CDK4/6 inhibitor palbociclib (Ibrance®) and a recipient of the American Chemical Society Heroes in Chemistry Award.
Lead discovery
- Pharmacology and drug discovery research
Duration:53min
Language:English
Session 1:presented October 1, 2024