October 23, 2025

Augmented Design: Practical Applications of LLMs and Agentic Workflows in Medicinal Chemistry

Large language models (LLMs) and agentic workflows are rapidly evolving from research curiosities into practical tools for day-to-day drug discovery. Rather than replacing scientific expertise, these systems now serve as collaborative partners—able to summarize literature, parse PDB structures, propose hypotheses, and chain analytical tools in real time.

This webinar brings together Jeff Blaney, PhD (Genentech) and Garry Pairaudeau, PhD (Dalton Tx) for a discussion on how chemists are applying LLMs and agentic AI systems to improve design throughput, explore a broader hypothesis space, and accelerate decision-making without compromising rigor or interpretability.

The session will outline where predictive modeling remains reliable, where LLMs add new value, and what a practical “agentic workflow” looks like inside modern medicinal chemistry programs.

Discussion Topics

Baseline for AI in Chemistry: What predictive modeling already does well (e.g., DMPK, physicochemical predictions) and where limits remain.
LLMs as Design Augmenters: How scientists are using LLMs for real-time ideation, hypothesis generation, and code-assisted analysis.
Agentic Workflows in Practice: Examples of LLM-driven agents retrieving data, integrating predictive models, and coordinating analyses across tools.
Augmentation over Automation: Designing AI systems that enhance human reasoning instead of replacing it.
Applied Takeaways: Practical starting points for teams looking to implement LLM- or agent-based approaches.

Tag(s): Webinars , CDD Blog

Augmented Design: Practical Applications of LLMs and Agentic Workflows in Medicinal Chemistry

Discussion Topics

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