Corpus ID: 235417265

ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction

  title={ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction},
  author={Xiaomin Fang and Lihang Liu and Jieqiong Lei and Donglong He and Shanzhuo Zhang and Jingbo Zhou and Fan Wang and Hua Wu and Haifeng Wang},
Effective molecular representation learning is of great importance to facilitate molecular property prediction, which is a fundamental task for the drug and material industry. Recent advances in graph neural networks (GNNs) have shown great promise in applying GNNs for molecular representation learning. Moreover, a few recent studies have also demonstrated successful applications of self-supervised learning methods to pre-train the GNNs to overcome the problem of insufficient labeled molecules… Expand
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  • Zeren Shui, G. Karypis
  • Computer Science, Physics
  • 2020 IEEE International Conference on Data Mining (ICDM)
  • 2020
A novel graph representation of molecules, heterogeneous molecular graph (HMG) in which nodes and edges are of various types, to model many-body interactions and achieves state-of-the-art performance in 9 out of 12 tasks on the QM9 dataset. Expand
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