Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction
Ao Shen,
Mingzhi Yuan,
Yingfan Ma
et al.
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 S… Show more
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