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Xiangxin Zhou

18 papers · 2019–2025 · 5 conferences · across top CS/AI conferences

Achievements

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+9 more ↓ πŸƒ Academic Marathon (6) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) 🐝 Cross-Pollinator (10)
🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (22) πŸŒ‰ Interdisciplinary Bridge πŸ† Keyword Champion (2) πŸ‘₯ Mega-Team (29) πŸ† Grand Slam πŸ‘‘ Triple Crown ⚑ Prolific Year (7) πŸ’Ž Century Club (18)

Conferences

ICLR (6) ICML (6) NIPS (4) AAAI (1) ACL (1)

Papers

Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling ICML 2025 OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use ACL 2025 ProteinBench: A Holistic Evaluation of Protein Foundation Models ICLR 2025 Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows ICLR 2025 Group Ligands Docking to Protein Pockets ICLR 2025 UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery ICLR 2025 An All-Atom Generative Model for Designing Protein Complexes ICML 2025 Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training ICML 2025 DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization ICLR 2024 Interaction-based Retrieval-augmented Diffusion Models for Protein-specific 3D Molecule Generation ICML 2024 Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process ICML 2024 Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization NIPS 2024 Binding-Adaptive Diffusion Models for Structure-Based Drug Design AAAI 2024 Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models ICLR 2024 Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design NIPS 2024 GSLB: The Graph Structure Learning Benchmark NIPS 2023 DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design ICML 2023 Global Sparse Momentum SGD for Pruning Very Deep Neural Networks NIPS 2019