4 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
Geometric Flow Matching for Molecular Conformation Generation via Manifold Decomposition
Yunqing Liu, Yi Zhou, Wenqi Fan
The generation of accurate 3D molecular conformations is a pivotal challenge in computational chemistry and drug discovery. Recently, diffusion and flow matching models have achiev…
Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions
Yunqing Liu, Yi Zhou, Wenqi Fan
Molecule representation learning is crucial for understanding and predicting molecular properties. However, conventional atom-centric models, which treat chemical bonds merely as p…
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen +6
Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require caref…
How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
Xinnan Dai, Haohao Qu, Yifen Shen +6
Benchmarking the capabilities and limitations of large language models (LLMs) in graph-related tasks is becoming an increasingly popular and crucial area of research. Recent studie…
Sub-graph Based Diffusion Model for Link Prediction
Hang Li, Wei Jin, Geri Skenderi +4
Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities in both synthesis and maximizing the data likelihood…