486 citations · 910 across the 5 of their papers we have counts for
5 papers
Q-Drug: a Framework to bring Drug Design into Quantum Space using Deep Learning
Zhaoping Xiong, Xiaopeng Cui, Xinyuan Lin +5
Optimizing the properties of molecules (materials or drugs) for stronger toughness, lower toxicity, or better bioavailability has been a long-standing challenge. In this context, w…
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…
Strategies for Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes +4
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific lab…
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You, Bowen Liu, Rex Ying +2
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This i…
Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar +7
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end t…