12 citations · 19 across the 18 of their papers we have counts for
20 papers
Fused Bayesian Flow Networks for Dual-Target Molecular Design
Jingyuan Zhou, Shikui Tu, Lei Xu
Dual-target drug design aims to generate 3D molecules that can simultaneously interact with two target proteins, offering a promising route for discovering polypharmacological comp…
PepLLM: ESM-Guided Llama for Structured Protein-Peptide Binding Interface Analysis
Hao Qian, Shikui Tu, Lei Xu
Protein-peptide interactions are central to cellular regulation and peptide-based drug discovery, yet existing computational methods mainly focus on interaction classification, bin…
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
Guang Lin, Shikui Tu, Lei Xu
Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).…
CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations
Zixian Su, Hongkai Zhang, Fan Gao +12
Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on…
Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration
Fengyang Xiao, Peng Hu, Lei Xu +7
Real-world image restoration aims to restore high-quality (HQ) images from degraded low-quality (LQ) inputs captured under uncontrolled conditions. Existing methods typically depen…
UniRank: End-to-End Domain-Specific Reranking of Hybrid Text-Image Candidates
Yupei Yang, Lin Yang, Wanxi Deng +3
Reranking is a critical component in many information retrieval pipelines. Despite remarkable progress in text-only settings, multimodal reranking remains challenging, particularly…
Factored Causal Representation Learning for Robust Reward Modeling in RLHF
Yupei Yang, Lin Yang, Wanxi Deng +5
A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. However, standard reward models a…
Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
Hao Qian, Pu You, Lin Zeng +5
Glioblastoma (GBM) remains the most aggressive tumor, urgently requiring novel therapeutic strategies. Here, we present a dry-to-wet framework combining generative modeling and exp…
Full-Atom Peptide Design via Riemannian-Euclidean Bayesian Flow Networks
Hao Qian, Shikui Tu, Lei Xu
Diffusion and flow matching models have recently emerged as promising approaches for peptide binder design. Despite their progress, these models still face two major challenges. Fi…
Text to Sketch Generation with Multi-Styles
Tengjie Li, Shikui Tu, Lei Xu
Recent advances in vision-language models have facilitated progress in sketch generation. However, existing specialized methods primarily focus on generic synthesis and lack mechan…
Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number
Jingyuan Zhou, Hao Qian, Shikui Tu +1
Structure-based drug design (SBDD), aiming to generate 3D molecules with high binding affinity toward target proteins, is a vital approach in novel drug discovery. Although recent…
Hallucination-Resistant Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement
Yupei Yang, Fan Feng, Lin Yang +5
Relation extraction (RE) enables the construction of structured knowledge for many downstream applications. While large language models (LLMs) have shown great promise in this task…
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge
Yupei Yang, Biwei Huang, Shikui Tu +1
The effectiveness of model training heavily relies on the quality of available training resources. However, budget constraints often impose limitations on data collection efforts.…
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
Yupei Yang, Biwei Huang, Fan Feng +3
General intelligence requires quick adaption across tasks. While existing reinforcement learning (RL) methods have made progress in generalization, they typically assume only distr…
PFB-Diff: Progressive Feature Blending Diffusion for Text-driven Image Editing
Wenjing Huang, Shikui Tu, Lei Xu
Diffusion models have demonstrated their ability to generate diverse and high-quality images, sparking considerable interest in their potential for real image editing applications.…
Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch Representation
Sicong Zang, Shikui Tu, Lei Xu
Graphic sketch representations are effective for representing sketches. Existing methods take the patches cropped from sketches as the graph nodes, and construct the edges based on…
IA-FaceS: A Bidirectional Method for Semantic Face Editing
Wenjing Huang, Shikui Tu, Lei Xu
Semantic face editing has achieved substantial progress in recent years. Known as a growingly popular method, latent space manipulation performs face editing by changing the latent…
Deep Rival Penalized Competitive Learning for Low-resolution Face Recognition
Peiying Li, Shikui Tu, Lei Xu
Current face recognition tasks are usually carried out on high-quality face images, but in reality, most face images are captured under unconstrained or poor conditions, e.g., by v…
Solve Traveling Salesman Problem by Monte Carlo Tree Search and Deep Neural Network
Zhihao Xing, Shikui Tu, Lei Xu
We present a self-learning approach that combines deep reinforcement learning and Monte Carlo tree search to solve the traveling salesman problem. The proposed approach has two adv…
A Comparative Study of Joint-SNVs Analysis Methods and Detection of Susceptibility Genes for Gastric Cancer in Korean Population
Jin-Xiong Lv, Shikui Tu, Lei Xu
Many joint-SNVs (single-nucleotide variants) analysis methods were proposed to tackle the "missing heritability" problem, which emphasizes that the joint genetic variants can expla…