ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2021
MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- A dual-branch graph neural network architecture for drug-target binding affinity prediction.Scientific reports · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Transformer-based graphs for drug-drug interaction with chemical knowledge embedding.Briefings in bioinformatics · 2026Article
- MMPCS: multi-view molecular pretraining based on consistency information and specific information.Bioinformatics (Oxford, England) · 2026Article
- HYG-mol: An Interpretable Multimodal Hypergraph Framework for Molecular Property Prediction.Computational and structural biotechnology journal · 2026Article
- KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions.Journal of chemical information and modeling · 2025Article
- Multimodal fusion with relational learning for molecular property prediction.Communications chemistry · 2025Article
- Recent advances in molecular representation methods and their applications in scaffold hopping.npj drug discovery · 2025Review
- Knowledge-aware contrastive heterogeneous molecular graph learning.PLoS computational biology · 2025Article
- Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction.Briefings in bioinformatics · 2025Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- Self-awareness of retrosynthesis via chemically inspired contrastive learning for reinforced molecule generation.Briefings in bioinformatics · 2025Article
- MolFCL: predicting molecular properties through chemistry-guided contrastive and prompt learning.Bioinformatics (Oxford, England) · 2025Article
- Information-theoretic multi-scale geometric pre-training for enhanced molecular property prediction.PloS one · 2025Article
- Graph contrastive learning of subcellular-resolution spatial transcriptomics improves cell type annotation and reveals critical molecular pathways.Briefings in bioinformatics · 2024Article
- Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds.Journal of chemical information and modeling · 2024Article
- Exploring Novel Fentanyl Analogues Using a Graph-Based Transformer Model.Interdisciplinary sciences, computational life sciences · 2024Article
- MvMRL: a multi-view molecular representation learning method for molecular property prediction.Briefings in bioinformatics · 2024Article
- MoleMCL: a multi-level contrastive learning framework for molecular pre-training.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Recent years have seen a rapid growth of utilizing graph neural networks (GNNs) in the biomedical domain for tackling drug-related problems. However, like any other deep architectures, GNNs are data hungry. While requiring labels in real world is often expensive, pretraining GNNs in an unsupervised manner has been actively explored. Among them, graph contrastive learning, by maximizing the mutual information between paired graph augmentations, has been shown to be effective on various downstream tasks. However, the current graph contrastive learning framework has two limitations. First, the augmentations are designed for general graphs and thus may not be suitable or powerful enough for certain domains. Second, the contrastive scheme only learns representations that are invariant to local perturbations and thus does not consider the global structure of the dataset, which may also be useful for downstream tasks. In this paper, we study graph contrastive learning designed specifically for the biomedical domain, where molecular graphs are present. We propose a novel framework called MoCL, which utilizes domain knowledge at both local- and global-level to assist representation learning. The local-level domain knowledge guides the augmentation process such that variation is introduced without changing graph semantics. The global-level knowledge encodes the similarity information between graphs in the entire dataset and helps to learn representations with richer semantics. The entire model is learned through a double contrast objective. We evaluate MoCL on various molecular datasets under both linear and semi-supervised settings and results show that MoCL achieves state-of-the-art performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.