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Property Prediction

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Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.

코드

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

https://github.com/chao1224/se3ddm

Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs

https://github.com/chenruduan/oareactdiff

A new perspective on building efficient and expressive 3D equivariant graph neural networks

https://github.com/cts198859/deeprl_dist

Neural Message Passing for Quantum Chemistry

https://github.com/cts198859/deeprl_network

Neural Message Passing for Quantum Chemistry

https://github.com/deepchem/deepchem

Self-Supervised Graph Transformer on Large-Scale Molecular Data

https://github.com/deepgraphlearning/esm-gearnet

Protein Representation Learning by Geometric Structure Pretraining

https://github.com/deepmodeling/Uni-Mol

Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

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

DeeperGCN: All You Need to Train Deeper GCNs

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/dongchen06/macacc

Neural Message Passing for Quantum Chemistry

https://github.com/espylapiza/Bag-of-Tricks-for-Node-Classification-with-Graph-Neural-Networks

Why Propagate Alone? Parallel Use of Labels and Features on Graphs

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

Molecule Property Prediction and Classification with Graph Hypernetworks

https://github.com/gcolmenarejo/cmd

Conditional molecular design with deep generative models

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

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

https://github.com/gmum/huggingmolecules

Relative Molecule Self-Attention Transformer

https://github.com/gmum/umwpl2021

Molecule Attention Transformer

https://github.com/gnn4dr/DRKG

Strategies for Pre-training Graph Neural Networks

https://github.com/hengruizhang98/InfoGraph

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

https://github.com/hld67890/prml2020_pj

Strategies for Pre-training Graph Neural Networks

https://github.com/ibm/materials

MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network

https://github.com/jacquesboitreaud/DeepFRED

Strategies for Pre-training Graph Neural Networks

https://github.com/jacquesboitreaud/rna_ne

Strategies for Pre-training Graph Neural Networks

https://github.com/jbr-ai-labs/lipophilicity-prediction

Analyzing Learned Molecular Representations for Property Prediction

https://github.com/jiangshunyu/SMF-GIN

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

https://github.com/jingsonglv/PSG

Path-aware Siamese Graph Neural Network for Link Prediction

https://github.com/juliagast/tgb2

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/keyhsw/chemllm

ChemLLM: A Chemical Large Language Model

https://github.com/laixinn/tgb

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/leffff/any-domain-pretrain-gnns

Strategies for Pre-training Graph Neural Networks

https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb_eff/ogbn_arxiv_dgl

DeeperGCN: All You Need to Train Deeper GCNs

https://github.com/longodj/tcvaemolgen

Path-Augmented Graph Transformer Network

https://github.com/lucidrains/molecule-attention-transformer

Molecule Attention Transformer

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