Knowledge-aware Contrastive Molecular Graph Learning

• URL: http://arxiv.org/abs/2103.13047v1
• Date: Wed, 24 Mar 2021 08:55:08 GMT
• Title: Knowledge-aware Contrastive Molecular Graph Learning
• Authors: Yin Fang, Haihong Yang, Xiang Zhuang, Xin Shao, Xiaohui Fan and Huajun Chen
• Abstract summary: We propose Contrastive Knowledge-aware GNN (CKGNN) for self-supervised molecular representation learning. We explicitly encode domain knowledge via knowledge-aware molecular encoder under the contrastive learning framework. Experiments on 8 public datasets demonstrate the effectiveness of our model with a 6% absolute improvement on average.
• Score: 5.08771973600915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging domain knowledge including fingerprints and functional groups in molecular representation learning is crucial for chemical property prediction and drug discovery. When modeling the relation between graph structure and molecular properties implicitly, existing works can hardly capture structural or property changes and complex structure, with much smaller atom vocabulary and highly frequent atoms. In this paper, we propose the Contrastive Knowledge-aware GNN (CKGNN) for self-supervised molecular representation learning to fuse domain knowledge into molecular graph representation. We explicitly encode domain knowledge via knowledge-aware molecular encoder under the contrastive learning framework, ensuring that the generated molecular embeddings equipped with chemical domain knowledge to distinguish molecules with similar chemical formula but dissimilar functions. Extensive experiments on 8 public datasets demonstrate the effectiveness of our model with a 6\% absolute improvement on average against strong competitors. Ablation study and further investigation also verify the best of both worlds: incorporation of chemical domain knowledge into self-supervised learning.

Related papers

• Knowledge-aware contrastive heterogeneous molecular graph learning [77.94721384862699]
  We propose a paradigm shift by encoding molecular graphs into Heterogeneous Molecular Graph Learning (KCHML) KCHML conceptualizes molecules through three distinct graph views-molecular, elemental, and pharmacological-enhanced by heterogeneous molecular graphs and a dual message-passing mechanism. This design offers a comprehensive representation for property prediction, as well as for downstream tasks such as drug-drug interaction (DDI) prediction.
  arXiv  Detail & Related papers  (2025-02-17T11:53:58Z)
• Molecular Graph Representation Learning via Structural Similarity Information [11.38130169319915]
  We introduce the textbf Structural Similarity Motif GNN (MSSM-GNN), a novel molecular graph representation learning method. In particular, we propose a specially designed graph that leverages graph kernel algorithms to represent the similarity between molecules quantitatively. We employ GNNs to learn feature representations from molecular graphs, aiming to enhance the accuracy of property prediction by incorporating additional molecular representation information.
  arXiv  Detail & Related papers  (2024-09-13T06:59:10Z)
• Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation [10.025809630976065]
  This paper introduces a novel pre-training framework that learns robust and generalizable chemical knowledge. Our approach demonstrates competitive performance across various molecular property benchmarks.
  arXiv  Detail & Related papers  (2023-11-05T23:47:52Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [68.32093648671496]
  We introduce GODE, which accounts for the dual-level structure inherent in molecules. Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph. By pre-training two GNNs on different graph structures, GODE effectively fuses molecular structures with their corresponding knowledge graph substructures.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning [57.670845619155195]
  We introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA) ASBA addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information. Our method offers a more comprehensive way to learn representations for molecular property prediction and has broad potential in drug and material discovery applications.
  arXiv  Detail & Related papers  (2023-05-22T00:56:00Z)
• A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language [63.60376252491507]
  We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data. We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
  arXiv  Detail & Related papers  (2022-09-12T00:56:57Z)
• Molecular Contrastive Learning with Chemical Element Knowledge Graph [16.136921143416927]
  Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. We construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph.
  arXiv  Detail & Related papers  (2021-12-01T15:04:39Z)
• Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery [53.00288162642151]
  We develop MoleculeKit, a suite of comprehensive machine learning tools spanning different computational models and molecular representations. Built on these representations, MoleculeKit includes both deep learning and traditional machine learning methods for graph and sequence data. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that MoleculeKit achieves consistent improvements over prior methods.
  arXiv  Detail & Related papers  (2020-12-02T02:09:31Z)
• ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction [61.33144688400446]
  We propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules. In the teacher model, we propose a novel semi-supervised learning method to learn general representation that jointly exploits information from molecular structure and molecular distribution. At last, we proposed a novel active learning strategy in terms of molecular diversities to select informative data during the whole framework learning.
  arXiv  Detail & Related papers  (2020-07-07T04:22:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.