Medication Recommendation via Dual Molecular Modalities and Multi-Substructure Enhancement

• URL: http://arxiv.org/abs/2405.20358v2
• Date: Tue, 9 Jul 2024 03:13:12 GMT
• Title: Medication Recommendation via Dual Molecular Modalities and Multi-Substructure Enhancement
• Authors: Shi Mu, Shunpan Liang, Xiang Li,
• Abstract summary: Existing works based on molecular knowledge neglect the 3D geometric structure of molecules. We propose BiMoRec, which introduces 3D molecular structures to obtain atomic 3D coordinates and edge indices. We use deep learning networks to construct a pretraining method that acquires 2D and 3D molecular structure representations and substructure representations.
• Score: 5.027701313370709
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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