9 citations · 15 across the 20 of their papers we have counts for
6 papers · 1 filter
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.…
AlloGen: Conformation-Selective Binder Generation with Differential State Scoring
Hanqun Cao, Zachary Quinn, Aastha Pal +4
Protein binder design has largely optimized for affinity alone, leaving conformational selectivity unaddressed: for allosteric targets such as kinases, nuclear receptors, and GPCRs…
TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
Hanqun Cao, Aastha Pal, Sophia Tang +4
Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This…
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…
Bi-TEAM: A Unified Cross-Scale Representation Learning Framework for Chemically Modified Biomolecules
Chunbin Gu, Zijun Gao, Mutian He +8
Representation learning for protein biochemical space faces a difficult trade-off: protein language models excel at capturing long-range biological semantics but often miss fine-gr…
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…