Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction
- URL: http://arxiv.org/abs/2405.02628v1
- Date: Sat, 4 May 2024 10:09:27 GMT
- Title: Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction
- Authors: Zexing Zhao, Guangsi Shi, Xiaopeng Wu, Ruohua Ren, Xiaojun Gao, Fuyi Li,
- Abstract summary: We introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction.
DIG-Mol integrates a momentum distillation network with two interconnected networks to efficiently improve molecular characterization.
We have established DIG-Mol's state-of-the-art performance through extensive experimental evaluation in a variety of molecular property prediction tasks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Molecular property prediction is a key component of AI-driven drug discovery and molecular characterization learning. Despite recent advances, existing methods still face challenges such as limited ability to generalize, and inadequate representation of learning from unlabeled data, especially for tasks specific to molecular structures. To address these limitations, we introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction. This architecture leverages the power of contrast learning with dual interaction mechanisms and unique molecular graph enhancement strategies. DIG-Mol integrates a momentum distillation network with two interconnected networks to efficiently improve molecular characterization. The framework's ability to extract key information about molecular structure and higher-order semantics is supported by minimizing loss of contrast. We have established DIG-Mol's state-of-the-art performance through extensive experimental evaluation in a variety of molecular property prediction tasks. In addition to demonstrating superior transferability in a small number of learning scenarios, our visualizations highlight DIG-Mol's enhanced interpretability and representation capabilities. These findings confirm the effectiveness of our approach in overcoming challenges faced by traditional methods and mark a significant advance in molecular property prediction.
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) - MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures [2.5563339057415218]
MolIG is a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures.
It amalgamates the strengths of both molecular representation forms.
It exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups.
arXiv Detail & Related papers (2023-11-28T10:28:35Z) - 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) - KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular
Property Prediction [13.55018269009361]
We introduce Knowledge-guided Pre-training of Graph Transformer (KPGT), a novel self-supervised learning framework for molecular graph representation learning.
KPGT can offer superior performance over current state-of-the-art methods on several molecular property prediction tasks.
arXiv Detail & Related papers (2022-06-02T08:22:14Z) - Attention-wise masked graph contrastive learning for predicting
molecular property [15.387677968070912]
We proposed a self-supervised representation learning framework for large-scale unlabeled molecules.
We developed a novel molecular graph augmentation strategy, referred to as attention-wise graph mask.
Our model can capture important molecular structure and higher-order semantic information.
arXiv Detail & Related papers (2022-05-02T00:28:02Z) - Few-Shot Graph Learning for Molecular Property Prediction [46.60746023179724]
We propose Meta-MGNN, a novel model for few-shot molecular property prediction.
To exploit unlabeled molecular information, Meta-MGNN further incorporates molecular structure, attribute based self-supervised modules and self-attentive task weights.
Extensive experiments on two public multi-property datasets demonstrate that Meta-MGNN outperforms a variety of state-of-the-art methods.
arXiv Detail & Related papers (2021-02-16T01:55:34Z) - 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) - Multi-View Graph Neural Networks for Molecular Property Prediction [67.54644592806876]
We present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture.
In MV-GNN, we introduce a shared self-attentive readout component and disagreement loss to stabilize the training process.
We further boost the expressive power of MV-GNN by proposing a cross-dependent message passing scheme.
arXiv Detail & Related papers (2020-05-17T04:46:07Z)
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.