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Molecular Property Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 4
평가표: 18
보존된 평가 기록: 224

Molecular property prediction is the task of predicting the properties of a molecule from its structure.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin

How Powerful are Graph Neural Networks?

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gin

How Powerful are Graph Neural Networks?

https://github.com/dmlc/dgl/tree/master/examples/pytorch/infograph

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dongchen06/macacc

Neural Message Passing for Quantum Chemistry

https://github.com/dpstart/graphormer_new

Do Transformers Really Perform Bad for Graph Representation?

https://github.com/dptech-corp/Uni-Mol

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/egyptdj/graph-neural-mapping

How Powerful are Graph Neural Networks?

https://github.com/emartinezs44/SparkGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/fanzhenliu/dagad

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/fongyk/graph-attention

Graph Attention Networks

https://github.com/fransou/is-meta-learning-necessary

Strategies for Pre-training Graph Neural Networks

https://github.com/fredjo89/heterogeneous-mpnn

Neural Message Passing for Quantum Chemistry

https://github.com/fromjade/dlmpnn

Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation

https://github.com/gajeelSF/Reinforcement-Learning-Branch-and-Bound

Neural Message Passing for Quantum Chemistry

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/giuseppefutia/link-prediction-code

Graph Attention Networks

https://github.com/gmum/graph-representations

Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction

https://github.com/gmum/umwpl2021

Graph Attention Networks

https://github.com/gnn4dr/DRKG

Strategies for Pre-training Graph Neural Networks

https://github.com/gordicaleksa/pytorch-GAT

Graph Attention Networks

https://github.com/gospodima/extended-simgnn

How Powerful are Graph Neural Networks?

https://github.com/guillaumejaume/tuto-dl-on-graphs

How Powerful are Graph Neural Networks?

https://github.com/hamed1375/exphormer

Recipe for a General, Powerful, Scalable Graph Transformer

https://github.com/handasontam/GAT-with-edgewise-attention

Graph Attention Networks

https://github.com/hazdzz/GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/hengruizhang98/InfoGraph

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.