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Home/Authors/Haitao Lin

Haitao Lin

Topic trends: 32,543 papers · similarity ≥ 0.4 · year ≥ 2024 · Data sourced from Semantic Scholar

34,598 papers | Abstracts: 31,650 (91.5%) | Citations: 34,598 (100.0%) | arXiv: 26,074 (75.4%)

Built: Feb 14, 2026, 11:22 PM AMS

19

papers

519

total citations

papers (19)

MogaNet: Multi-order Gated Aggregation Network

SAR-Net: Shape Alignment and Recovery Network for Category-Level 6D Object Pose and Size Estimation

Neural Pose Transfer by Spatially Adaptive Instance Normalization

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration

NEURIPS 2023arXiv

MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding

PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching

GeoVLN: Learning Geometry-Enhanced Visual Representation With Slot Attention for Vision-and-Language Navigation

Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph

PourIt!: Weakly-Supervised Liquid Perception from a Single Image for Visual Closed-Loop Robotic Pouring

Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning

dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen

DaCapo: Score Distillation as Stacked Bridge for Fast and High-quality 3D Editing

CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image

Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization

GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation

3515 Protein 3D Graph Structure Learning for Robust Structure-Based Protein Property Prediction

Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks