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Gang Du

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#artificial intelligence Preprint Oct 2026

CODesign: Consistency from Data to Trajectory in All-Atom Protein Binder Co-Design

The central challenge in de novo protein design is generating plausible, mutually compatible structures and sequences, such that each designed sequence folds into its intended structure and the structure accommodates that sequence. Compared to typical two-stage design methods, which decouple the modeling of the interde...

Yuan-Le Mo, Bo Qiang, Hai-Tao Lin et al. · 0 citations
Preprint Aug 2026

EpiBench: Can LLMs Understand Epitopes for Antibody Drug Discovery?

The results show that current LLMs capture partial epitope-related signals but remain limited in antibody-specific sequence grounding, long-context residue localization, and biologically grounded reasoning, so EpiBench provides a diagnostic testbed for measuring and improving sequence-aware biomedical LLMs toward relia...

Zi-Rui Wang, Jiaqing Wang, Qing-Han Wang et al. · 0 citations
Open access Aug 2026

NACraft: Programmatic nucleic-acid aptamer design via all-atom structure-model feedback

NACraft, a training-free and programmatic framework for all-atom nucleic-acid aptamer design based on backpropagation through structure-model feedback, is presented, demonstrating the effectiveness and versatility of NACraft and extending structure-model hallucination toward programmatic nucleic-acid aptamer design.

He-Qin Zhu, Jiaqi Wang, Wei-Bo Zhao et al. · 0 citations

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