3 papers
cs.AI2026
SEISMO: Explanation-Aware, Trajectory-Conditioned LLM Agents for Sample-Efficient Molecular Optimisation
Fabian P. Krüger, Fabian P. Krüger, Andrea Hunklinger +4
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.BM2025
Guiding Generative Protein Language Models with Reinforcement Learning
Filippo Stocco, Maria Artigues-Lleixa, Andrea Hunklinger +3
Protein language models (pLMs) have demonstrated success at generating functional proteins across vast sequence spaces but lack the ability to design high-fitness variants on deman…