In the past few decades, polymers, high-molecular-weight compounds formed by
bonding numerous identical or similar monomers covalently, have played an
essential role in various scientific fields. In this context, accurate
prediction of their properties is becoming increasingly crucial. Typically, the
properties of a polymer, such as plasticity, conductivity, bio-compatibility,
and so on, are highly correlated with its 3D structure. However, current
methods for predicting polymer properties heavily rely on information from
polymer SMILES sequences (P-SMILES strings) while ignoring crucial 3D
structural information, leading to sub-optimal performance. In this work, we
propose MMPolymer, a novel multimodal multitask pretraining framework
incorporating both polymer 1D sequential information and 3D structural
information to enhance downstream polymer property prediction tasks. Besides,
to overcome the limited availability of polymer 3D data, we further propose the
"Star Substitution" strategy to extract 3D structural information effectively.
During pretraining, MMPolymer not only predicts masked tokens and recovers 3D
coordinates but also achieves the cross-modal alignment of latent
representation. Subsequently, we further fine-tune the pretrained MMPolymer for
downstream polymer property prediction tasks in the supervised learning
paradigm. Experimental results demonstrate that MMPolymer achieves
state-of-the-art performance in various polymer property prediction tasks.
Moreover, leveraging the pretrained MMPolymer and using only one modality
(either P-SMILES string or 3D conformation) during fine-tuning can also surpass
existing polymer property prediction methods, highlighting the exceptional
capability of MMPolymer in polymer feature extraction and utilization. Our
online platform for polymer property prediction is available at
this https URL

该研究提出了 MMPolymer，一种新颖的多模态多任务预训练框架，结合了聚合物的 1D 顺序信息和 3D 结构信息，以增强下游聚合物性质预测任务。实验证明，MMPolymer 在各种聚合物性质预测任务中实现了最先进的性能。