ArticleBiomolecules2023
Molecular Property Prediction by Combining LSTM and GAT.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- MolGramTreeNet: A multimodal molecular property prediction model via grammar tree-constrained molecular representation.iScience · 2026Article
- CMMSCL-DPI: cross-modal multi-structural contrastive learning for predicting drug-protein interactions.BMC biology · 2026Article
- Efficient Learning of Molecular Properties Using Graph Neural Networks Enhanced with Chemistry Knowledge.ACS omega · 2025Article
- SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction.PLoS computational biology · 2025Article
- Deep learning for property prediction of natural fiber polymer composites.Scientific reports · 2025Article
- Enhancing molecular property prediction with quantized GNN models.Journal of cheminformatics · 2025Article
- ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation.Journal of chemical theory and computation · 2025Article
- A machine learning method for predicting molecular antimicrobial activity.Scientific reports · 2025Article
- MIPPIS: protein-protein interaction site prediction network with multi-information fusion.BMC bioinformatics · 2024Article
- MvMRL: a multi-view molecular representation learning method for molecular property prediction.Briefings in bioinformatics · 2024Article
- Triple Generative Self-Supervised Learning Method for Molecular Property Prediction.International journal of molecular sciences · 2024Article
- MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular property prediction is an important direction in computer-aided drug design. In this paper, to fully explore the information from SMILE stings and graph data of molecules, we combined the SALSTM and GAT methods in order to mine the feature information of molecules from sequences and graphs. The embedding atoms are obtained through SALSTM, firstly using SMILES strings, and they are combined with graph node features and fed into the GAT to extract the global molecular representation. At the same time, data augmentation is added to enlarge the training dataset and improve the performance of the model. Finally, to enhance the interpretability of the model, the attention layers of both models are fused together to highlight the key atoms. Comparison with other graph-based and sequence-based methods, for multiple datasets, shows that our method can achieve high prediction accuracy with good generalizability.
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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.