The 8th SCS Syngenta Symposium explored how artificial intelligence is transforming the practice of chemistry. AI-augmented chemistry amplifies human expertise, empowering researchers to navigate infinite chemical spaces, identify non-obvious solutions, and focus their creative energy on where it matters most. The program highlighted these breakthroughs, featuring generative models for small molecule generation, de novo protein design, AI-guided retrosynthesis and cutting-edge machine learning approaches to property prediction. These technical sessions are grounded in real-world examples of how augmented workflows are currently accelerating discovery in both academic and industrial settings.
After the welcome message by the local hosts and symposium organizers, Amandine Kolleth and Fides Benfatti, both from the Syngenta research team in Stein, the morning lectures were given by Asst. Prof. Patrick Bryant, Stockholm University, «De novo design of functional peptides with non-natural chemistry», Dr. Nadine Schneider, Novartis, «AI-augmented chemistry: from generative models to decision-making in drug discovery», Prof. Martin Pačesa, University of Zurich, «Probing biology using computational binder design» and Dr. Teodoro Laino, IBM Zurich,
«From molecular transformers to quantum-enabled chemical discovery».
During Lunch break the event offered the opportunity to visit the poster session or to network in the inspiring surroundings of the Syngenta Research Center.
After lunch another three lectures competed the attractive program. The lectures were given by Prof. Philippe Schwaller, EPFL Lausanne, «Chemistry in the age of foundation models», Prof. Leroy Cronin, University of Glasgow, «Agentic Verification of Chemical Makeability and Synthesis with Chemputation» and Prof. Abigail G. Doyle, UCLA, «Enabling chemical synthesis via machine learning»
The symposium concluded with closing remarks and the Best Poster Award ceremony.
The winners of the Best Poster Award are:
Yves Grandjean, University of Bern
«RXNMapperv2: updated validation and evaluation on expanded chemical space»
Omid Mokhtari, University of Lorraine
«Dynamics-Aware Geometric Deep Learning for Protein-Protein and Protein- Ligand Binding Site Prediction»
Photos copyrights: Synganta
David Spichiger, SCS
28.10.2026