ArticleACS omega2024
Meta Learning with Attention Based FP-GNNs for Few-Shot Molecular Property Prediction.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- HD-SKRG: a hierarchical dual-view and structure-knowledge relation graph enhancement network for few-shot molecular property prediction.Molecular diversity · 2026Article
- Boosting the Prediction Accuracy of Glass Transition Temperature in Polyimides: A Hybrid Machine Learning Approach Integrating Morgan Fingerprints and Molecular Descriptors.Molecular informatics · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Meta-Learning as a Promising Strategy for Lipid Nanoparticle Optimization and Ionizable Lipid Discovery.Nano letters · 2026Article
- Exploring Chemoinformatics Aspects of Few-Shot Meta-Learning by Example of an Infinite Dilution Activity Coefficient in Ionic Liquid Prediction.Journal of chemical information and modeling · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Bayesian Meta-Learning for Few-Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins.Angewandte Chemie (International ed. in English) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular property prediction holds significant importance in drug discovery, enabling the identification of biologically active compounds with favorable drug-like properties. However, the low data problem, arising from the scarcity of labeled data in drug discovery, poses a substantial obstacle for accurate predictions. To address this challenge, we introduce a novel architecture, AttFPGNN-MAML, for few-shot molecular property prediction. The proposed approach incorporates a hybrid feature representation to enrich molecular representations and model intermolecular relationships specific to the task. By leveraging ProtoMAML, a meta-learning strategy, our model is trained and adapted to new tasks. Evaluation on two few-shot data sets, MoleculeNet and FS-Mol, demonstrates our method's superior performance in three out of four tasks and across various support set sizes. These results convincingly validate the effectiveness of our method in the realm of few-shot molecular property prediction. The source code is publicly available at https://github.com/sanomics-lab/AttFPGNN-MAML.
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Registered trials
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