1 citations · 2 across the 12 of their papers we have counts for
14 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).…
DS2SC-Agent: A Multi-Agent Automated Pipeline for Rapid Chiplet Model Generation
Yiwei Wu, Yifan Wu, Yunhao Xiong +6
Constructing behavioral-level chiplet models (e.g., SystemC) is crucial for early-stage heterogeneous architecture exploration. Traditional manual modeling is notoriously time-cons…
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…
THFlow: A Temporally Hierarchical Flow Matching Framework for 3D Peptide Design
Dengdeng Huang, Shikui Tu
Deep generative models provide a promising approach to de novo 3D peptide design. Most of them jointly model the distributions of peptide's position, orientation, and conformation,…
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…