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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/mfmceneaney/Lambda-GNNs

How Powerful are Graph Neural Networks?

https://github.com/microljy/graph_level_drug_discovery

MoleculeNet: A Benchmark for Molecular Machine Learning

https://github.com/microsoft/Graphormer

Do Transformers Really Perform Bad for Graph Representation?

https://github.com/microsoft/fs-mol

Strategies for Pre-training Graph Neural Networks

https://github.com/mindspore-ai/models/tree/master/official/gnn/gat

Graph Attention Networks

https://github.com/mitya8128/experiments_notes

Graph Attention Networks

https://github.com/mkorshe/awesome_awesome_chemoinformatics

MoleculeNet: A Benchmark for Molecular Machine Learning

https://github.com/mlzxzhou/keras-gnm

Graph Attention Networks

https://github.com/negarhdr/PGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/nieci2024/pyglcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/nnaakkaaii/g2-MLP

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2

Graph Attention Networks

https://github.com/nrel/m2p

Neural Message Passing for Quantum Chemistry

https://github.com/nslab-cuk/halal-or-not

Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck

https://github.com/pengsl-lab/mssl

Graph Attention Networks

https://github.com/pengsl-lab/mssl2drug

Graph Attention Networks

https://github.com/pfnet-research/treewidth-prediction

How Powerful are Graph Neural Networks?

https://github.com/priba/nmp_qc

Neural Message Passing for Quantum Chemistry

https://github.com/priba/siamese_ged

Neural Message Passing for Quantum Chemistry

https://github.com/qema/orca-py

Graph Attention Networks

https://github.com/rusty1s/pytorch_geometric

Principal Neighbourhood Aggregation for Graph Nets

https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/nn/conv/gatv2_conv.py

How Attentive are Graph Attention Networks?

https://github.com/selmiss/gp-tlstgcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/shamim-hussain/egt_pytorch

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

https://github.com/shamim-hussain/egt_triangular

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

https://github.com/shenweichen/GraphNeuralNetwork

Graph Attention Networks

https://github.com/shi27feng/transformers.satisfy

Graph Attention Networks

https://github.com/snap-stanford/pretrain-gnns

Strategies for Pre-training Graph Neural Networks

https://github.com/snowkylin/gnn

Graph Attention Networks

https://github.com/snownus/COOP

Graph Attention Networks

https://github.com/steshinss/lohi_neurips2023

Lo-Hi: Practical ML Drug Discovery Benchmark

https://github.com/steshinss/lohi_splitter

Lo-Hi: Practical ML Drug Discovery Benchmark

https://github.com/subercui/pyGConvAT

Graph Attention Networks

https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/taishan1994/pytorch_gat

Graph Attention Networks

https://github.com/tatp22/pytorch-fast-GAT

Graph Attention Networks

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