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.

Related papers

• Molecular Property Prediction Based on Graph Structure Learning [29.516479802217205]
  We propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. With molecular fingerprints, we construct a molecular similarity graph (MSG)
  arXiv  Detail & Related papers  (2023-12-28T06:45:13Z)
• MolGrapher: Graph-based Visual Recognition of Chemical Structures [50.13749978547401]
  We introduce MolGrapher to recognize chemical structures visually. We treat all candidate atoms and bonds as nodes and put them in a graph. We classify atom and bond nodes in the graph with a Graph Neural Network.
  arXiv  Detail & Related papers  (2023-08-23T16:16:11Z)
• Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning [1.3717673827807508]
  We study how molecular property information can be transferred from natural language to graph representations. We implement neural relevance scoring strategies to improve text retrieval, introduce a novel chemically-valid molecular graph augmentation strategy. We achieve a +4.26% AUROC gain versus models pre-trained on the graph modality alone, and a +1.54% gain compared to recently proposed molecular graph/text contrastively trained MoMu model.
  arXiv  Detail & Related papers  (2023-07-22T10:32:58Z)
• 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)
• Conditional Graph Information Bottleneck for Molecular Relational Learning [9.56625683182106]
  We propose a novel relational learning framework, CGIB, that predicts the interaction behavior between a pair of graphs by detecting core subgraphs therein. Our proposed method mimics the nature of chemical reactions, i.e., the core substructure of a molecule varies depending on which other molecule it interacts with.
  arXiv  Detail & Related papers  (2023-04-29T01:17:43Z)
• 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)
• Chemical-Reaction-Aware Molecule Representation Learning [88.79052749877334]
  We propose using chemical reactions to assist learning molecule representation. Our approach is proven effective to 1) keep the embedding space well-organized and 2) improve the generalization ability of molecule embeddings. Experimental results demonstrate that our method achieves state-of-the-art performance in a variety of downstream tasks.
  arXiv  Detail & Related papers  (2021-09-21T00:08:43Z)
• 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)
• Self-Supervised Graph Transformer on Large-Scale Molecular Data [73.3448373618865]
  We propose a novel framework, GROVER, for molecular representation learning. GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
  arXiv  Detail & Related papers  (2020-06-18T08:37:04Z)

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.