  Medication recommendation combines patient medical history with biomedical
knowledge to assist doctors in determining medication combinations more
accurately and safely. Existing works based on molecular knowledge neglect the
3D geometric structure of molecules and fail to learn the high-dimensional
information of medications, leading to structural confusion. Additionally, it
does not extract key substructures from a single patient visit, resulting in
the failure to identify medication molecules suitable for the current patient
visit. To address the above limitations, we propose a bimodal molecular
recommendation framework named BiMoRec, which introduces 3D molecular
structures to obtain atomic 3D coordinates and edge indices, overcoming the
inherent lack of high-dimensional molecular information in 2D molecular
structures. To retain the fast training and prediction efficiency of the
recommendation system, we use bimodal graph contrastive pretraining to maximize
the mutual information between the two molecular modalities, achieving the
fusion of 2D and 3D molecular graphs and re-evaluating substructures at the
visit level. Specifically, we use deep learning networks to construct a
pretraining method that acquires 2D and 3D molecular structure representations
and substructure representations, and obtain mutual information through
contrastive learning. We then generate fused molecular representations using
the trained GNN module and re-determine the relevance of substructure
representations in combination with the patient's clinical history. Finally, we
generate the final medication combination based on the extracted substructure
sequences. Our implementation on the MIMIC-III and MIMIC-IV datasets
demonstrates that our method achieves state-of-the-art performance. Compared to
the second-best baseline, our model improves accuracy by 2.07%, with DDI at the
same level as the baseline.
