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Periodicity-aware deep learning for polymers
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AI
DOI：10.1038/s43588-025-00903-9.png)
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Abstract
En 中文
Deep learning has revolutionized chemical research by accelerating the discovery and understanding of complex chemical systems. However, polymer chemistry lacks a unified deep learning framework owing to the complexity of polymer structures. Existing self-supervised learning methods simplify polymers into repeating units and neglect their inherent periodicity, thereby limiting the models’ ability to generalize across tasks. To address this, we propose a periodicity-aware deep learning framework for polymers, PerioGT. In pre-training, a chemical knowledge-driven periodicity prior is constructed and incorporated into the model through contrastive learning. Then, periodicity prompts are learned in fine-tuning based on the prior. Additionally, a graph augmentation strategy is employed, which integrates additional conditions via virtual nodes to model complex chemical interactions. PerioGT achieves state-of-the-art performance on 16 downstream tasks. Wet-lab experiments highlight PerioGT’s potential in the real world, identifying two polymers with potent antimicrobial properties. Our results demonstrate that introducing the periodicity prior effectively enhances model performance. PerioGT is a self-supervised learning framework for polymer property prediction, integrating periodicity priors and additional conditions to enhance generalization under data scarcity and enable broad applicability.
Keywords:
Periodicity-aware deep learning
Polymer property prediction
Self-supervised learning
Graph augmentation
Chemical knowledge prior
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