# MIPS: a Multimodal Infinite Polymer Sequence Pre-training Framework for Polymer Property Prediction

arXiv: https://arxiv.org/abs/2507.20326 · HTML: https://mlsanity.com/paper/2507.20326/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 63 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Builds on (most cited first, 20 of 20)

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- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
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- [Molecular representation learning with language models and domain-relevant auxiliary tasks](https://mlsanity.com/paper/2011.13230/) (2020)
- [Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets](https://mlsanity.com/paper/2203.04810/) (2022)
- [polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics](https://mlsanity.com/paper/2209.14803/) (2022)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [Automated 3D Pre-Training for Molecular Property Prediction](https://mlsanity.com/paper/2306.07812/) (2023)
- [MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction](https://mlsanity.com/paper/2406.04727/) (2024)
- [Expressivity and Generalization: Fragment-Biases for Molecular GNNs](https://mlsanity.com/paper/2406.08210/) (2024)
- [Transferring a molecular foundation model for polymer property predictions](https://mlsanity.com/paper/2310.16958/) (2023)

## Abstract (arXiv)

Polymers, composed of repeating structural units called monomers, are fundamental materials in daily life and industry. Accurate property prediction for polymers is essential for their design, development, and application. However, existing modeling approaches, which typically represent polymers by the constituent monomers, struggle to capture the whole properties of polymer, since the properties change during the polymerization process. In this study, we propose a Multimodal Infinite Polymer Sequence (MIPS) pre-training framework, which represents polymers as infinite sequences of monomers and integrates both topological and spatial information for comprehensive modeling. From the topological perspective, we generalize message passing mechanism (MPM) and graph attention mechanism (GAM) to infinite polymer sequences. For MPM, we demonstrate that applying MPM to infinite polymer sequences is equivalent to applying MPM on the induced star-linking graph of monomers. For GAM, we propose to further replace global graph attention with localized graph attention (LGA). Moreover, we show the robustness of the "star linking" strategy through Repeat and Shift Invariance Test (RSIT). Despite its robustness, "star linking" strategy exhibits limitations when monomer side chains contain ring structures, a common characteristic of polymers, as it fails the Weisfeiler-Lehman~(WL) test. To overcome this issue, we propose backbone embedding to enhance the capability of MPM and LGA on infinite polymer sequences. From the spatial perspective, we extract 3D descriptors of repeating monomers to capture spatial information. Finally, we design a cross-modal fusion mechanism to unify the topological and spatial information. Experimental validation across eight diverse polymer property prediction tasks reveals that MIPS achieves state-of-the-art performance.