3 citations · 4 across the 3 of their papers we have counts for
8 papers
2DNMRGym: An Annotated Experimental Dataset for Atom-Level Molecular Representation Learning in 2D NMR via Surrogate Supervision
Yunrui Li, Hao Xu, Pengyu Hong
Two-dimensional (2D) Nuclear Magnetic Resonance (NMR) spectroscopy, particularly Heteronuclear Single Quantum Coherence (HSQC) spectroscopy, plays a critical role in elucidating mo…
Multimodal Fusion with Relational Learning for Molecular Property Prediction
Zhengyang Zhou, Yunrui Li, Pengyu Hong +1
Graph based molecular representation learning is essential for accurately predicting molecular properties in drug discovery and materials science; however, it faces significant cha…
Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active Nematics
Phu N. Tran, Sattvic Ray, Linnea Lemma +6
Deep learning-based optical flow (DLOF) extracts features in adjacent video frames with deep convolutional neural networks. It uses those features to estimate the inter-frame motio…
TransPeakNet: Solvent-Aware 2D NMR Prediction via Multi-Task Pre-Training and Unsupervised Learning
Yunrui Li, Hao Xu, Ambrish Kumar +4
Nuclear Magnetic Resonance (NMR) spectroscopy is essential for revealing molecular structure, electronic environment, and dynamics. Accurate NMR shift prediction allows researchers…
Graph Multi-Similarity Learning for Molecular Property Prediction
Hao Xu, Zhengyang Zhou, Pengyu Hong
Enhancing accurate molecular property prediction relies on effective and proficient representation learning. It is crucial to incorporate diverse molecular relationships characteri…
Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment
Hao Xu, Zhengyang Zhou, Pengyu Hong
Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise…