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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/lucidrains/slot-attention

Object-Centric Learning with Slot Attention

https://github.com/luis-mueller/wl-transformers

On the Stability of Expressive Positional Encodings for Graphs

https://github.com/lukecavabarrett/pna

Neural Message Passing for Quantum Chemistry

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

Strategies for Pre-training Graph Neural Networks

https://github.com/mouthful/ClofNet

GEOM: Energy-annotated molecular conformations for property prediction and molecular generation

https://github.com/nrel/m2p

Neural Message Passing for Quantum Chemistry

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/samsungsailmontreal/stgg-al

Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees

https://github.com/scikit-fingerprints/peptides_molecular_fingerprints_classification

Molecular Fingerprints Are Strong Models for Peptide Function Prediction

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/snap-stanford/graphgym

Identity-aware Graph Neural Networks

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

Strategies for Pre-training Graph Neural Networks

https://github.com/sraghavan0610/SSVAE

Conditional molecular design with deep generative models

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/superlouis/GATGNN

Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction

https://github.com/teddykoker/e3nn.c

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials

https://github.com/teddykoker/mpnn-for-quantum-chem

Neural Message Passing for Quantum Chemistry

https://github.com/tencent-alchemy/Alchemy

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

https://github.com/thumnlab/autogl-light

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

https://github.com/timpostuvan/CTDG-link-anomaly-detection

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/timpostuvan/tgb-link-anomaly-detection-baselines

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/tingweidaniel/chemprop_atomicfp_save_depth_final

Analyzing Learned Molecular Representations for Property Prediction

https://github.com/tomdbar/eco-dqn

Neural Message Passing for Quantum Chemistry

https://github.com/untitled-ai/slot_attention

Object-Centric Learning with Slot Attention

https://github.com/xnuohz/DeeperGCN-dgl

DeeperGCN: All You Need to Train Deeper GCNs

https://github.com/yangzhao1230/graphtextretrieval

A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language

https://github.com/ycremar/DIG-SSL

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

https://github.com/yujiabao/ls

Learning to Split for Automatic Bias Detection

https://github.com/yuwvandy/g2gnn

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

https://github.com/zhitao-wang/PLNLP

Pairwise Learning for Neural Link Prediction

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