WEBINAR:

Using AI and Retrosynthesis

To Streamline Small Molecule Discovery

September 9th 2025
11:00am - 12:00pm ET 
on AI & Lab Software Digital Summit from Lab Manager from September 9-10 2025

AI and digital workflows are reshaping how small molecules are designed, evaluated, and synthesized. In this webinar, we present a retrospective case study showing how a published small molecule, originally discovered through traditional methods, could be identified more efficiently using a fully in silico approach.

We’ll walk through an integrated digital Design–Make–Test–Analyze (DMTA) cycle using AIDDISON™ for generative molecule design and predictive ADMET modelling, and SYNTHIA® for retrosynthetic and library synthesis planning. This iterative workflow enables faster decision-making, better compound prioritization, and closer alignment between design and synthesis.

Attendees will gain practical insight into how AI and lab software can enhance early-stage discovery and improve the efficiency of small molecule R&D.

Learning Objectives:

  1. Learn how an integrated digital workflow can enhance hit-to-lead and hit candidate optimization
  2. Explore applications for generating diverse molecule libraries with desired drug-like properties
  3. See how retrosynthetic and forward synthesis planning can refine de novo designs for real-world feasibility and speed to synthesis

Speaker

Suhasini M Iyengar, Ph.D. 
Application and Discovery Scientist – AI and Cheminformatics

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.

Emma Gardener, Ph. D.
Technical Application Scientist, Cheminformatics Technologies

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 as a Technical Application Scientist and is responsible for the commercial licensing of the retrosynthetic design software, SYNTHIA®.