Bi-level Contrastive Learning for Knowledge-Enhanced Molecule
Representations
- URL: http://arxiv.org/abs/2306.01631v4
- Date: Sat, 20 Jan 2024 03:22:43 GMT
- Title: Bi-level Contrastive Learning for Knowledge-Enhanced Molecule
Representations
- Authors: Pengcheng Jiang, Cao Xiao, Tianfan Fu, Jimeng Sun
- Abstract summary: We propose a novel method called GODE, which takes into account the two-level structure of individual molecules.
By pre-training two graph neural networks (GNNs) on different graph structures, combined with contrastive learning, GODE fuses molecular structures with their corresponding knowledge graph substructures.
When fine-tuned across 11 chemical property tasks, our model outperforms existing benchmarks, registering an average ROC-AUC uplift of 13.8% for classification tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
- Score: 55.42602325017405
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Molecule representation learning is crucial for various downstream
applications, such as understanding and predicting molecular properties and
side effects. In this paper, we propose a novel method called GODE, which takes
into account the two-level structure of individual molecules. We recognize that
molecules have an intrinsic graph structure as well as being a node in a larger
molecule knowledge graph. GODE integrates graph representations of individual
molecules with multidomain biochemical data from knowledge graphs. By
pre-training two graph neural networks (GNNs) on different graph structures,
combined with contrastive learning, GODE fuses molecular structures with their
corresponding knowledge graph substructures. This fusion results in a more
robust and informative representation, which enhances molecular property
prediction by harnessing both chemical and biological information. When
fine-tuned across 11 chemical property tasks, our model outperforms existing
benchmarks, registering an average ROC-AUC uplift of 13.8% for classification
tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
Impressively, it surpasses the current leading model in molecule property
predictions with average advancements of 2.1% in classification and 6.4% in
regression tasks.
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