ArticleComputational intelligence and neuroscience2022
A Novel Molecular Representation Learning for Molecular Property Prediction with a Multiple SMILES-Based Augmentation.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Multi-Modal Fusion Frameworks of Subgraph-Optimized Graph Autoencoder for Molecular Property Prediction.Journal of chemical information and modeling · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- Identification of novel triazolopyrimidines as potent α-glucosidase inhibitor through design, synthesis, biological evaluations, and computational analysis.Scientific reports · 2025Article
- Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning.Journal of chemical information and modeling · 2025Article
- Deep Supramolecular Language Processing for Co-Crystal Prediction.Angewandte Chemie (International ed. in English) · 2025Article
- A hitchhiker's guide to deep chemical language processing for bioactivity prediction.Digital discovery · 2025Article
- AI alignment is all your need for future drug discovery.Frontiers in artificial intelligence · 2025Review
- Predicting transcriptional changes induced by molecules with MiTCP.Briefings in bioinformatics · 2024Article
- Attribute-guided prototype network for few-shot molecular property prediction.Briefings in bioinformatics · 2024Article
- A focus on molecular representation learning for the prediction of chemical properties.Chemical science · 2024Article
- An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks.Biomolecules · 2024Article
- Molecular Property Prediction by Combining LSTM and GAT.Biomolecules · 2023Article
- On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach.Computational and structural biotechnology journal · 2022Review
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4 authors.
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Abstract
Deep learning has brought a rapid development in the aspect of molecular representation for various tasks, such as molecular property prediction. The prediction of molecular properties is a crucial task in the field of drug discovery for finding specific drugs with good pharmacological activity and pharmacokinetic properties. SMILES string is always used as a kind of character approach in deep neural network models, inspired by natural language processing techniques. However, the deep learning models are hindered by the nonunique nature of the SMILES string. To efficiently learn molecular features along all message paths, in this paper we encode multiple SMILES for every molecule as an automated data augmentation for the prediction of molecular properties, which alleviates the overfitting problem caused by the small amount of data in the datasets of molecular property prediction. As a result, by using the multiple SMILES-based augmentation, we obtained better molecular representation and showed superior performance in the tasks of predicting molecular properties.
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