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DropConn: Dropout Connection Based Random GNNs for Molecular Property Prediction

  • Published:2023 Issue: Volume: Page:1-13
  • ISSN:1041-4347
  • Container-title:IEEE Transactions on Knowledge and Data Engineering
  • language:
  • Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Zhang Dan1ORCID,Feng Wenzheng1,Wang Yuandong1,Qi Zhongang2,Shan Ying2,Tang Jie1ORCID

Affiliation:

1. Department of Computer Science and Technology, Tsinghua Univerisity, China

2. Tencent, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Reference48 articles.

1. Machine learning for chemical discovery

2. Relational inductive biases, deep learning, and graph networks;Battaglia,2018

3. Directional graph networks;Beaini

4. MixMatch: A holistic approach to semi-supervised learning;Berthelot,2019

5. Graph convolutions that can finally model local structure;Brossard,2020

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1. Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey;Big Data Mining and Analytics;2024-09

2. Effective Entry-Wise Flow for Molecule Generation;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

3. RecDCL: Dual Contrastive Learning for Recommendation;Proceedings of the ACM Web Conference 2024;2024-05-13

4. Mix-Key: graph mixup with key structures for molecular property prediction;Briefings in Bioinformatics;2024-03-27
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