Papers with Code 아카이브

Atomistic Description

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코드

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

https://github.com/AIRI-Institute/conditional-crystal-generation

공식 코드

Unleashing the power of novel conditional generative approaches for new materials discovery

https://github.com/AIRI-Institute/nablaDFT

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$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

https://github.com/AbhiroopBhattacharya/MatMMFuse

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MatMMFuse: Multi-Modal Fusion model for Material Property Prediction

https://github.com/Augus1999/bayesian-flow-network-for-chemistry

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A Bayesian Flow Network Framework for Chemistry Tasks

https://github.com/BrightBlueCheese/transformers_and_chemistry

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The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA

https://github.com/CRIPAC-DIG/Pin-Tuning

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Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction

https://github.com/CederGroupHub/chgnet

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CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling

https://github.com/Cortys/rankgnn

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Ranking Structured Objects with Graph Neural Networks

https://github.com/Data-reindeer/MOCO

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Improving Molecular Pretraining with Complementary Featurizations

https://github.com/FedML-AI/FedGraphNN

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FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

https://github.com/FedML-AI/SpreadGNN

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SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks

https://github.com/GSK-AI/mocop

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Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation

https://github.com/Graph-COM/SPE

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On the Stability of Expressive Positional Encodings for Graphs

https://github.com/HaoZhongkai/AS_Molecule

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ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

https://github.com/Hugh415/HimNet

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Learning Hierarchical Interaction for Accurate Molecular Property Prediction

https://github.com/Hyacinth-YX/mixture-of-collaborative-experts

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Enhancing Molecular Property Prediction via Mixture of Collaborative Experts

https://github.com/Innixma/autogluon-benchmarking

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AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

https://github.com/JinA0218/drugood-densify

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Robust Molecular Property Prediction via Densifying Scarce Labeled Data

https://github.com/Microsoft/Graphormer

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Do Transformers Really Perform Bad for Graph Representation?

https://github.com/MilesZhao/PGCGM

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Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints

https://github.com/NSAPH-Projects/topological-equivariant-networks

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E(n) Equivariant Topological Neural Networks

https://github.com/NSLab-CUK/S-CGIB

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

https://github.com/Nanolbw/SuGAr

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Subgraph Aggregation for Out-of-Distribution Generalization on Graphs

https://github.com/PaddlePaddle/PaddleHelix

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GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions

https://github.com/PetarV-/GAT

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Graph Attention Networks

https://github.com/SINGROUP/dscribe

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DScribe: Library of Descriptors for Machine Learning in Materials Science

https://github.com/TommyDzh/RingFormer

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RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction

https://github.com/TrustAI/testRNN

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testRNN: Coverage-guided Testing on Recurrent Neural Networks

https://github.com/Vencent-Won/KRGTS-public

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Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction

https://github.com/Wenlin-Chen/ADKF-IFT

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Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction

https://github.com/XieResearchGroup/Physics-aware-Multiplex-GNN

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Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes

https://github.com/YKQ98/Matformer

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Periodic Graph Transformers for Crystal Material Property Prediction

https://github.com/Yinghao-Li/UncertaintyBenchmark

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MUBen: Benchmarking the Uncertainty of Molecular Representation Models

https://github.com/ZJU-Fangyin/KCL

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Molecular Contrastive Learning with Chemical Element Knowledge Graph

https://github.com/ZeXingZ/DIG-Mol

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Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction

https://github.com/Zhenger959/MoleculeCLA

공식 코드

MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis

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