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Found 9 papers, 3 papers with code
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules.
Equivalent Distance Geometry Error for Molecular Conformation Comparison
Straight-forward conformation generation models, which generate 3-D structures directly from input molecular graphs, play an important role in various molecular tasks with machine learning, such as 3D-QSAR and virtual screening in drug design.
HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks
To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs).
Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
Learning Node Representations from Noisy Graph Structures
Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting.
A Novel User Representation Paradigm for Making Personalized Candidate Retrieval
Our work is undergoing a anonymous review, and it will soon be released after the notification.
GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes.
SepNE: Bringing Separability to Network Embedding
We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes.
