3 papers
cs.AI2026
SEISMO: Explanation-Aware, Trajectory-Conditioned LLM Agents for Sample-Efficient Molecular Optimisation
Fabian P. Krüger, Andrea Hunklinger, Adrian Wolny +3
Optimizing molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industry, where it underlies the discove…
q-bio.BM2025
Toward the Explainability of Protein Language Models
Andrea Hunklinger, Noelia Ferruz
Protein language models (pLMs) excel in a variety of tasks that range from structure prediction to the design of functional enzymes. However, these models operate as black boxes, a…
q-bio.BM2024
Reinforcement Learning Guides Generative Protein Language Models
Filippo Stocco, Maria Artigues-Lleixa, Andrea Hunklinger +4
Protein engineering can optimize molecules for biotechnology and therapeutics, but navigating the high-dimensional sequence landscape remains challenging. Protein language models (…