ArticleBriefings in bioinformatics2024
Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-View Biomedical Foundation Models for Molecule-Target and Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MolCL-SP: a multimodal contrastive learning framework with non-overlapping substructure perturbations for molecular property prediction.Bioinformatics (Oxford, England) · 2025Article
- Recent advances in molecular representation methods and their applications in scaffold hopping.npj drug discovery · 2025Review
- ProteinF3S: boosting enzyme function prediction by fusing protein sequence, structure, and surface.Briefings in bioinformatics · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Self-supervised learning plays an important role in molecular representation learning because labeled molecular data are usually limited in many tasks, such as chemical property prediction and virtual screening. However, most existing molecular pre-training methods focus on one modality of molecular data, and the complementary information of two important modalities, SMILES and graph, is not fully explored. In this study, we propose an effective multi-modality self-supervised learning framework for molecular SMILES and graph. Specifically, SMILES data and graph data are first tokenized so that they can be processed by a unified Transformer-based backbone network, which is trained by a masked reconstruction strategy. In addition, we introduce a specialized non-overlapping masking strategy to encourage fine-grained interaction between these two modalities. Experimental results show that our framework achieves state-of-the-art performance in a series of molecular property prediction tasks, and a detailed ablation study demonstrates efficacy of the multi-modality framework and the masking strategy.
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