1 citations · 1 across the 4 of their papers we have counts for
6 papers
A Pre-trained Reaction Embedding Descriptor Capturing Bond Transformation Patterns
Weiqi Liu, Fenglei Cao, Yuan Qi +1
With the rise of data-driven reaction prediction models, effective reaction descriptors are crucial for bridging the gap between real-world chemistry and digital representations. H…
Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Xuan Zhang, Zhijian Zhou, Weidi Xu +3
Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network's…
Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Zhijian Zhou, Junyi An, Zongkai Liu +5
Generating physically realistic 3D molecular structures remains a core challenge in molecular generative modeling. While diffusion models equipped with equivariant neural networks…
Equivariant Spherical Transformer for Efficient Molecular Modeling
Junyi An, Xinyu Lu, Chao Qu +6
Equivariant Graph Neural Networks (GNNs) have significantly advanced the modeling of 3D molecular structure by leveraging group representations. However, their message passing, hea…
Equivariant Masked Position Prediction for Efficient Molecular Representation
Junyi An, Chao Qu, Yun-Fei Shi +4
Graph neural networks (GNNs) have shown considerable promise in computational chemistry. However, the limited availability of molecular data raises concerns regarding GNNs' ability…
ChemHTS: Hierarchical Tool Stacking for Enhancing Chemical Agents
Zhucong Li, Jin Xiao, Bowei Zhang +5
Large Language Models (LLMs) have demonstrated remarkable potential in scientific research, particularly in chemistry-related tasks such as molecular design, reaction prediction, a…