1.5k citations · 6.9k across the 102 of their papers we have counts for
8 papers · 1 filter
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
Kexin Huang, Tianfan Fu, Dawood Khan +7
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable progress in predicting drug ef…
Subgraph Neural Networks
Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li +1
Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks. However, despite the proliferation of the methods and their suc…
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Xiang Zhang, Marinka Zitnik
Deep learning methods for graphs achieve remarkable performance across a variety of domains. However, recent findings indicate that small, unnoticeable perturbations of graph struc…
Graph Meta Learning via Local Subgraphs
Kexin Huang, Marinka Zitnik
Prevailing methods for graphs require abundant label and edge information for learning. When data for a new task are scarce, meta-learning can learn from prior experiences and form…
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik +5
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML…
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
Kexin Huang, Cao Xiao, Lucas Glass +2
Molecular interaction networks are powerful resources for the discovery. They are increasingly used with machine learning methods to predict biologically meaningful interactions. W…