Papers with Code 아카이브
Molecular Property Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/Microsoft/gated-graph-neural-network-samples
https://github.com/Mind23-2/MindCode-4/tree/main/gat
https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU
https://github.com/Namkyeong/NoisyNodes_Pytorch
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
https://github.com/Negus25/GAT-Mindspore
https://github.com/PumpkinYing/GAT
https://github.com/PurdueMINDS/RelationalPooling
https://github.com/Samyu0304/thought-propagation
https://github.com/Saro00/DGN
https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
https://github.com/Tony-Y/cgnn
https://github.com/TyngJiunKuo/deep-learning-project
https://github.com/WantingZhao/my_GAT
https://github.com/XuHangkun/Compound-Protein-Interaction
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
https://github.com/Yindong-Zhang/myGAT
https://github.com/YunseobShin/wiki_GAT
https://github.com/ZJULearning/graph_level_drug_discovery
https://github.com/ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/abhilash1910/GraphAttentionNetworks
https://github.com/abi-kothapalli/minimumdominatingsets
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ai-hpc-research-team/git-mol
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
https://github.com/ai-hpc-research-team/slm4mol
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
https://github.com/ajbisberg/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ak24watch/Graph-Deeplearning-GAT
https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network
https://github.com/akianfar/Deep-CBN
https://github.com/alexOarga/haiku-geometric
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py
https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/pna_conv.py
https://github.com/alililia/ms_contrib/tree/main/ascend_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alililia/ms_contrib/tree/main/gpu_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/anish-lu-yihe/SVRT-by-GAT
https://github.com/anonymous20201002/chemprop
Analyzing Learned Molecular Representations for Property Prediction
https://github.com/arangoml/fastgraphml
https://github.com/asarigun/GraphMixerNetworks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.