Asymmetric contrastive multimodal learning for advancing chemical understanding
We propose an asymmetric contrastive multimodal learning framework, an effective and training-efficient framework tailored for molecules, promoting cross-modality understanding between the molecular graph and other chemical modalities.
Nov 11, 2023
Motif-based graph representation learning with application to chemical molecules
We designed a novel convolution module for graph representational learning on molecules with an efficient pretraining strategy, enabling the capture of local structural and semantic information from graph motifs.
Jan 11, 2023