6 citations · 27 across the 40 of their papers we have counts for
7 papers · 1 filter
DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models
Liang Wang, Yu Rong, Tingyang Xu +7
Molecular structure elucidation from spectra is a fundamental challenge in molecular science. Conventional approaches rely heavily on expert interpretation and lack scalability, wh…
Graffe: Graph Representation Learning via Diffusion Probabilistic Models
Dingshuo Chen, Shuchen Xue, Liuji Chen +5
Diffusion probabilistic models (DPMs), widely recognized for their potential to generate high-quality samples, tend to go unnoticed in representation learning. While recent progres…
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Liang Wang, Shaozhen Liu, Yu Rong +3
Establishing the relationship between 3D structures and the energy states of molecular systems has proven to be a promising approach for learning 3D molecular representations. Howe…
Diffusion Models for Molecules: A Survey of Methods and Tasks
Liang Wang, Chao Song, Zhiyuan Liu +3
Generative tasks about molecules, including but not limited to molecule generation, are crucial for drug discovery and material design, and have consistently attracted significant…
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
Liang Wang, Qiang Liu, Shaozhen Liu +2
Molecular property prediction (MPP) is integral to drug discovery and material science, but often faces the challenge of data scarcity in real-world scenarios. Addressing this, few…
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization
Dingshuo Chen, Zhixun Li, Yuyan Ni +6
With the emergence of various molecular tasks and massive datasets, how to perform efficient training has become an urgent yet under-explored issue in the area. Data pruning (DP),…