1 citations · 1 across the 12 of their papers we have counts for
13 papers
DegradeQuery: Counterfactual Tuple Pretraining for Context-Aware PROTAC Degradation Prediction
Dong Xu, Zhangfan Yang, Jiantao Wu +3
Proteolysis-targeting chimeras (PROTACs) induce protein degradation by recruiting a target protein to an E3 ubiquitin ligase, making degradation a joint outcome of the degrader mol…
MVis-Fold: A Three-Dimensional Microvascular Structure Inference Model for Super-Resolution Ultrasound
Jincao Yao, Ke Zhang, Yahan Zhou +8
Super-resolution ultrasound (SRUS) technology has overcome the resolution limitations of conventional ultrasound, enabling micrometer-scale imaging of microvasculature. However, du…
BioLM-Score: Language-Prior Conditioned Probabilistic Geometric Potentials for Protein-Ligand Scoring
Zhangfan Yang, Baoyun Chen, Dong Xu +4
Protein-ligand scoring is a central component of structure-based drug design, underpinning molecular docking, virtual screening, and pose optimization. Conventional physics-based e…
RIGA-Fold: A General Framework for Protein Inverse Folding via Recurrent Interaction and Geometric Awareness
Sisi Yuan, Jiehuang Chen, Junchuang Cai +4
Protein inverse folding, the task of predicting amino acid sequences for desired structures, is pivotal for de novo protein design. However, existing GNN-based methods typically su…
Unveiling Scaling Behaviors in Molecular Language Models: Effects of Model Size, Data, and Representation
Dong Xu, Qihua Pan, Sisi Yuan +3
Molecular generative models, often employing GPT-style language modeling on molecular string representations, have shown promising capabilities when scaled to large datasets and mo…
From Tokens to Blocks: A Block-Diffusion Perspective on Molecular Generation
Qianwei Yang, Dong Xu, Zhangfan Yang +4
Drug discovery can be viewed as a combinatorial search over an immense chemical space, motivating the development of deep generative models for de novo molecular design. Among thes…
Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning
Dong Xu, Jiantao Wu, Qihua Pan +3
Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing…
Toward Closed-loop Molecular Discovery via Language Model, Property Alignment and Strategic Search
Junkai Ji, Zhangfan Yang, Dong Xu +4
Drug discovery is a time-consuming and expensive process, with traditional high-throughput and docking-based virtual screening hampered by low success rates and limited scalability…
IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Dong Xu, Zhangfan Yang, Jenna Xinyi Yao +3
Three-dimensional generative models increasingly drive structure-based drug discovery, yet it remains constrained by the scarce publicly available protein-ligand complexes. Under s…
FROGENT: An End-to-End Full-process Drug Design Multi-Agent System
Qihua Pan, Dong Xu, Qianwei Yang +5
Drug discovery is a complex, multi-step pipeline that remains heavily dependent on manual, experience-driven operations; meanwhile, existing customized artificial intelligence tool…
MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation
Dong Xu, Zhangfan Yang, Sisi Yuan +3
Three-dimensional molecular generators based on diffusion models can now reach near-crystallographic accuracy, yet they remain fragmented across tasks. SMILES-only inputs, two-stag…
READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design
Dong Xu, Zhangfan Yang, Junchuang Cai +4
Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution. Howe…
A Dual-Directional Context-Aware Test-Time Learning for Text Classification
Dong Xu, Mengyao Liao, Zhenglin Lai +2
Text classification assigns text to predefined categories. Traditional methods struggle with complex structures and long-range dependencies. Deep learning with recurrent neural net…