Integrated In Silico Workflows for Small Molecule Discovery and Optimization
Webinar
In this webinar explore how AI and digital technologies are reshaping the landscape of drug discovery and optimization. We present a retrospective case study that reimagines the discovery of a published small molecule through a modern in silico workflow. By integrating generative design, predictive ADMET modeling, and retrosynthetic library planning, we demonstrate how early discovery efforts can be streamlined without compromising scientific integrity. Utilizing AIDDISON™ and SYNTHIA® software, this session showcases an iterative and integrated digital Design-Make-Test-Analyze (DMTA) cycle. Attendees will gain practical insights into how these tools work in tandem to reduce cycle times, prioritize synthetically accessible candidates, and enhance decision-making in small molecule discovery.
Key Takeaways:
- Learn how an integrated digital workflow can optimize hit-to-lead and candidate selection.
- Discover techniques for generating diverse molecule libraries with desirable drug-like properties.
- Understand how retrosynthetic and forward synthesis planning can refine de novo designs for real-world feasibility and accelerate synthesis timelines.
Speakers
Emma Gardener
Merck
Ph.D. Technical Application Scientist
Emma Gardener received her undergraduate degree from Trinity College and worked for several years as a research associate in the biotechnology industry before completing her Ph.D. in Organic Chemistry at Brown University as an NSF graduate research fellow. She studied under Prof. Jason Sello, where she worked on developing new methodology for the synthesis of antibacterial peptide natural products. In 2018, she joined the Cheminformatics Technologies department of Merck as a Technical Application Scientist and is responsible for the commercial licensing of the SYNTHIA® retrosynthetic design software.
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.
Chemistry and synthesis
- Lead discovery
Duration:1h
Language:English
Session 1:presented September 10, 2025