Molecular property prediction
- Neural Message Passing for Quantum Chemistry
- By: Gilmer, J. et al., 2017
- Journal: International Conference on Machine Learning, ICML 2017
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- Molecular contrastive learning of representations via graph neural networks
- Review Study: Application of message passing neural networks for molecular property prediction
- By: Tang, M. et al., 2023
- Journal: Elsevier, Current Opinion in Structural Biology
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- Review Study: A compact review of molecular property prediction with graph neural networks
- By: Weider, O. et al., 2020
- Journal: Elsevier, Drug Discovery Today
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- Analyzing Learned Molecular Representations for Property Prediction
- By: Yang, K. et al., 2019
- Journal: Journal of Chemical Information and Modeling
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- MD-GNN: A mechanism-data-driven graph neural network for molecular properties prediction and new material discovery
- By: Chen, S. et al., 2023
- Journal: Elsevier, Journal of Molecular Graphics and Modelling
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- Equivariant Graph Neural Networks for Toxicity Prediction
- By: Cremer, J. et al., 2023
- Journal: Chemical Research in Toxicology - American Chemical Society
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- Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models
- By: Aouichaoui, A. et al., 2023
- Journal: Journal of Chemical Information and Modeling - American Chemical Society
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- Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking
- ASGN: An active semi-supervised graph neural network for molecular property prediction
- By: Hao, Z. et al., 2020
- Conference: KDD ‘20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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- XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties
- By: Deng, D. et al., 2021
- Journal: Journal of Chemical Information and Modeling - American Chemical Society
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- Advanced graph and sequence neural networks for molecular property prediction and drug discovery
- Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
- By: Shui, Z. et al., 2020
- Conference: IEEE International Conference on Data Mining (ICDM)
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- HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention
- By: Zhu, W. et al., 2023
- Journal: Journal of Chemical Information and Modeling - American Chemical Society
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- Quantitative evaluation of explainable graph neural networks for molecular property prediction
- Cross-dependent graph neural networks for molecular property prediction
- Few-Shot Graph Learning for Molecular Property Prediction
- By: Guo, Z. et al., 2021
- Conference: WWW ‘21: Proceedings of the Web Conference 2021
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- GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction
- By: Guo, Z. et al., 2020
- Conference: CIKM ‘20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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- Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
- By: ZHANG, Z. et al., 2021
- Conference: Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
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- Molecular Geometry Prediction using a Deep Generative Graph Neural Network