Molecular Property Prediction
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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/Augus1999/bayesian-flow-network-for-chemistry
공식 코드https://github.com/BrightBlueCheese/transformers_and_chemistry
공식 코드https://github.com/CRIPAC-DIG/Pin-Tuning
공식 코드Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
https://github.com/Cortys/rankgnn
공식 코드https://github.com/Data-reindeer/MOCO
공식 코드Improving Molecular Pretraining with Complementary Featurizations
https://github.com/FedML-AI/FedGraphNN
공식 코드FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
https://github.com/FedML-AI/SpreadGNN
공식 코드SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
https://github.com/GSK-AI/mocop
공식 코드Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation
https://github.com/Graph-COM/SPE
공식 코드On the Stability of Expressive Positional Encodings for Graphs
https://github.com/HaoZhongkai/AS_Molecule
공식 코드ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
https://github.com/Hugh415/HimNet
공식 코드Learning Hierarchical Interaction for Accurate Molecular Property Prediction
https://github.com/Hyacinth-YX/mixture-of-collaborative-experts
공식 코드Enhancing Molecular Property Prediction via Mixture of Collaborative Experts
https://github.com/Innixma/autogluon-benchmarking
공식 코드AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/JinA0218/drugood-densify
공식 코드Robust Molecular Property Prediction via Densifying Scarce Labeled Data
https://github.com/Microsoft/Graphormer
공식 코드Do Transformers Really Perform Bad for Graph Representation?
https://github.com/NSAPH-Projects/topological-equivariant-networks
공식 코드https://github.com/NSLab-CUK/S-CGIB
공식 코드https://github.com/Nanolbw/SuGAr
공식 코드Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
https://github.com/PaddlePaddle/PaddleHelix
공식 코드https://github.com/PetarV-/GAT
공식 코드https://github.com/TommyDzh/RingFormer
공식 코드RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction
https://github.com/TrustAI/testRNN
공식 코드testRNN: Coverage-guided Testing on Recurrent Neural Networks
https://github.com/Vencent-Won/KRGTS-public
공식 코드Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction
https://github.com/Wenlin-Chen/ADKF-IFT
공식 코드Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
https://github.com/XieResearchGroup/Physics-aware-Multiplex-GNN
공식 코드https://github.com/Yinghao-Li/UncertaintyBenchmark
공식 코드MUBen: Benchmarking the Uncertainty of Molecular Representation Models
https://github.com/ZJU-Fangyin/KCL
공식 코드Molecular Contrastive Learning with Chemical Element Knowledge Graph
https://github.com/ZeXingZ/DIG-Mol
공식 코드Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction
https://github.com/Zhenger959/MoleculeCLA
공식 코드MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis
https://github.com/agave233/GeomGCL
공식 코드GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
https://github.com/ahmed-a-a-elhag/gad
공식 코드https://github.com/aidotse/leakpro
공식 코드Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy
https://github.com/amitaysicherman/reactembed
공식 코드https://github.com/andoree/smm4h_2021_classification
공식 코드Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification
https://github.com/arch4ngel21/scikit-fingerprints
공식 코드A Python library for efficient computation of molecular fingerprints
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.