11 papers
Exploring the Alignment of Generation and Understanding in Protein Structure Modeling
Junde Xu, Yuansheng Huang, Zijun Gao +5
Understanding and generation are often treated as two separate paradigms in training deep neural networks, despite the fact that both are trained with closely related objectives su…
SF-Cluster: Frustration-Guided MSA Subsampling for Alternative Protein Conformation Recovery
Hanqun Cao, Zijun Gao, Chunbin Gu +3
Deep-learning structure predictors are sensitive to their multiple sequence alignment (MSA) input, making MSA subsampling a practical route to recovering alternative conformations.…
GeoCycler: Reward-Aligned 3D Diffusion for Constraint-Conditioned Cyclic Peptide Design
Jingjie Zhang, Hanqun Cao, Haosen Shi +10
Cyclic peptides are attractive therapeutic modalities because their closed-ring topology can improve stability and target specificity. However, de novo cyclic peptide design remain…
CA-DEL: An Open Multi-Target, Multi-Modal Benchmark for Learning from DNA-Encoded Library Screens
Mutian He, Hanqun Cao, Cheng Tan +4
The success of machine learning in drug discovery hinges on learning the relationship between a chemical structure and its biological activity. While DNA-Encoded Library (DEL) tech…
CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
Zijun Gao, Mutian He, Shijia Sun +8
Reliable evaluation of protein structure predictions remains challenging, as metrics like pLDDT capture energetic stability but often miss subtle errors such as atomic clashes or c…
Learning the PTM Code through a Coarse-to-Fine, Mechanism-Aware Framework
Jingjie Zhang, Hanqun Cao, Zijun Gao +8
Post-translational modifications (PTMs) form a combinatorial "code" that regulates protein function, yet deciphering this code - linking modified sites to their catalytic enzymes -…