3 papers
cs.LG2026
Structural Hierarchy and Geometry in Molecular Representation Learning
David Sulu, Lorenzo Di Fruscia, Jana M. Weber
Molecular self-supervised learning uses chemical structures to guide which molecular embeddings should be similar. We study whether explicitly encoding a molecule's Bemis-Murcko sc…
cs.AI2026
Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval
Olga Bunkova, Lorenzo Di Fruscia, Sophia Rupprecht +3
Large Language Models (LLMs) can aid synthesis planning in chemistry, but standard prompting methods often yield hallucinated or outdated suggestions. We study LLM interactions wit…
cs.AI2025
Leveraging Large Language Models for enzymatic reaction prediction and characterization
Lorenzo Di Fruscia, Jana Marie Weber
Predicting enzymatic reactions is crucial for applications in biocatalysis, metabolic engineering, and drug discovery, yet it remains a complex and resource-intensive task. Large L…