3 papers
cs.AI2026
Confidence is the key: how conformal prediction enhances the generative design of permeable peptides
Laura van Weesep, Sunay Chankeshwara, Leonardo De Maria +3
Generative models coupled with reinforcement learning (RL), such as REINVENT and PepINVENT, have emerged as a powerful framework for de novo molecular design. During the ideation p…
q-bio.BM2026
ZeroFold: Protein-RNA Binding Affinity Predictions from Pre-Structural Embeddings
Josef Hanke, Sebastian Pujalte Ojeda, Shengyu Zhang +3
The accurate prediction of protein-RNA binding affinity remains an unsolved problem in structural biology, limiting opportunities in understanding gene regulation and designing RNA…
q-bio.BM2025
Mask prior-guided denoising diffusion improves inverse protein folding
Peizhen Bai, Filip MiljkoviÄ, Xianyuan Liu +4
Inverse protein folding generates valid amino acid sequences that can fold into a desired protein structure, with recent deep-learning advances showing strong potential and competi…