3 citations · 10 across the 7 of their papers we have counts for
7 papers
GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation
Shengyin Sun, Wenhao Yu, Yuxiang Ren +5
Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center I…
Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
Kaiwei Zhang, Yange Lin, Guangcheng Wu +5
The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming t…
A quatum inspired neural network for geometric modeling
Weitao Du, Shengchao Liu, Xuecang Zhang
By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance. In pa…
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
Weitao Du, Jiujiu Chen, Xuecang Zhang +2
Recently, artificial intelligence for drug discovery has raised increasing interest in both machine learning and chemistry domains. The fundamental building block for drug discover…
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs
Shengyin Sun, Yuxiang Ren, Jiehao Chen +1
The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP). Inspired by the success of LLMs in NLP tasks, some recen…
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs
Jianxiang Yu, Yuxiang Ren, Chenghua Gong +3
Text-attributed graphs have recently garnered significant attention due to their wide range of applications in web domains. Existing methodologies employ word embedding models for…
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
Tianyu Zhang, Yuxiang Ren, Wenzheng Feng +2
Unsupervised graph representation learning is a non-trivial topic. The success of contrastive methods in the unsupervised representation learning on structured data inspires simila…