Date: 1 December 2026 (Tuesday)
Time: 4pm GMT | 5pm CET | 11am ET | 8am PT
Ask 10 drug discovery scientists how they use large language models (LLMs), and you’ll get 10 different answers. Ask how far they trust them and the room goes quiet.
In under two years, LLMs have gone from novelty to daily habit:
- Reading papers
- Writing code
- Querying data
- Drafting reports
Now agentic AI is moving into discovery workflows. These systems plan:
- Multi-step tasks
- Call models
- And act on the results
But a slick demo is not something a project team can stake a decision on, and the gap between the two is still wide.
This panel skips the hype and takes an honest look at:
- What’s working now: literature triage, data wrangling, coding, model building, and experiment design
- What’s improving fast: reasoning, tool use, longer context, and working with scientific data and specialist predictive models
- What still falls short: chemical reasoning, quantitative reliability, reproducibility, and integration with proprietary data and systems
- What can bite you: hallucinated references and results, over-confidence, data security and IP, bias, and deskilling
- What’s next: autonomous design-make-test-analyse loops, the changing role of the scientist, and how to validate and govern AI agents
Underneath all of it sits one question: how do you know when an AI answer is good enough to act on, and what does it cost you when it isn’t?
Who it’s for:
- Chemists working out where to start
- Modellers deciding how far to trust an AI assistant
- Discovery leaders choosing where to invest
You’ll leave with a realistic view of what these tools can do today, where the traps are, and what to prepare for.
A candid panel on where AI is really changing discovery, and where it still lets you down.
Meet the speakers
Nathan Brown, PhD
Director of Science, Optibrium™
Nathan Brown leads Optibrium’s Research and Application Science teams, developing new methods while ensuring customer feedback directly shapes R&D priorities. He was previously Director of Digital Chemistry at Healx, where he led the development of AI-powered computational methods for rare disease drug discovery, and before that led the Cheminformatics team at BenevolentAI and founded the In Silico Medicinal Chemistry team at The Institute of Cancer Research, delivering significant impact on drugs in active clinical trials. A globally recognised thought leader in cheminformatics and computational chemistry, Nathan is a Fellow of the Royal Society of Chemistry and sits on the Editorial Advisory Boards of the Journal of Chemical Information and Modeling and ChemMedChem. He invented the first multi-objective de novo molecular design system, published in 2004, and has authored over 50 peer-reviewed papers and four books. He won the 2017 Corwin Hansch Award for his outstanding contributions to the field.
Srijit Seal, PhD
Principal Scientist, Human Chemical Company
Dr. Srijit Seal is a Principal Scientist at Human Chemical Company and a Visiting Researcher at Uppsala University. His research focuses on chemoinformatics and computational biology, particularly applying machine learning and Cell Painting to investigate small-molecule bioactivity, image-based profiling, and drug discovery. Previously, he was a Senior Scientist at Merck US and completed his postdoctoral research at the Broad Institute of MIT and Harvard, under the supervision of Anne Carpenter and Shantanu Singh. He obtained his PhD from the University of Cambridge under the supervision of Andreas Bender. He also serves on the Board of Directors of the American Society for Cellular and Computational Toxicology and as Associate Editor of the Journal of Cheminformatics.
Kostya Yatsenko
Tessel Biosciences