8 citations · 8 across the 1 of their papers we have counts for
2 papers
q-bio.BM2026
Steering Generative Models for Protein Design: Aligning and Conditioning Strategies
Filippo Stocco, Michele Garibbo, Noelia Ferruz
Generative artificial intelligence models learn probability distributions from data and produce novel samples that capture the salient properties of their training sets. Proteins a…
q-bio.BM2025★ 8 cited
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…