  Polymers are high-molecular-weight compounds constructed by the covalent
bonding of numerous identical or similar monomers so that their 3D structures
are complex yet exhibit unignorable regularity. Typically, the properties of a
polymer, such as plasticity, conductivity, bio-compatibility, and so on, are
highly correlated with its 3D structure. However, existing polymer property
prediction methods heavily rely on the information learned from polymer SMILES
sequences (P-SMILES strings) while ignoring crucial 3D structural information,
resulting in sub-optimal performance. In this work, we propose MMPolymer, a
novel multimodal multitask pretraining framework incorporating polymer 1D
sequential and 3D structural information to encourage downstream polymer
property prediction tasks. Besides, considering the scarcity of polymer 3D
data, we further introduce the "Star Substitution" strategy to extract 3D
structural information effectively. During pretraining, in addition to
predicting masked tokens and recovering clear 3D coordinates, MMPolymer
achieves the cross-modal alignment of latent representations. Then we further
fine-tune the pretrained MMPolymer for downstream polymer property prediction
tasks in the supervised learning paradigm. Experiments show that MMPolymer
achieves state-of-the-art performance in downstream property prediction tasks.
Moreover, given the pretrained MMPolymer, utilizing merely a single modality in
the fine-tuning phase can also outperform existing methods, showcasing the
exceptional capability of MMPolymer in polymer feature extraction and
utilization.
