ArticleBioinformatics (Oxford, England)2024
Molecular property prediction based on graph structure learning.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Bridging predictive reliability and explainability: a multi-representation deep learning framework for chemical space analysis of immune bioassays.Journal of cheminformatics · 2026Article
- TMolNet: a task-aware multimodal neural network for molecular property prediction.Molecular diversity · 2026Article
- Physics-guided GNN-transformer model for multi-scale fatigue life prediction of concrete track slabs in high-speed railways.Scientific reports · 2026Article
- Multimodal graph fusion with statistically guided parsimonious descriptor selection for molecular property prediction.Journal of cheminformatics · 2026Article
- SM-GAT: a safety-aware multi-task graph attention network for multi-target anti-diabetic lead discovery from natural products.Frontiers in molecular biosciences · 2026Article
- ProtoMol: enhancing molecular property prediction via prototype-guided multimodal learning.Briefings in bioinformatics · 2025Article
- A multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors.Journal of pharmaceutical analysis · 2025Article
- Recent advances in molecular representation methods and their applications in scaffold hopping.npj drug discovery · 2025Review
- Generalizable, fast, and accurate DeepQSPR with fastprop.Journal of cheminformatics · 2025Article
- Context-informed few-shot molecular property prediction via heterogeneous meta-learning.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationMolecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have achieved considerable progress in improving prediction performance. However, current models often ignore relationships between molecules, which could be also helpful for MPP.
resultsFor this sake, in this article 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. Then, with molecular fingerprints, we construct a molecule similarity graph (MSG). Following that, we conduct GSL on the MSG, i.e. molecule-level GSL, to get the final molecular embeddings, which are the results of fuzing both GNN encoded molecular representations and the relationships among molecules. That is, combining both intra-molecule and inter-molecule information. Finally, we use these molecular embeddings to perform MPP. Extensive experiments on 10 various benchmark datasets show that our method could achieve state-of-the-art performance in most cases, especially on classification tasks. Further visualization studies also demonstrate the good molecular representations of our method. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/zby961104/GSL-MPP.
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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.