MMM: Quantum-Chemical Molecular Representation Learning for Combinatorial Drug Recommendation

• URL: http://arxiv.org/abs/2510.07910v1
• Date: Thu, 09 Oct 2025 08:03:14 GMT
• Title: MMM: Quantum-Chemical Molecular Representation Learning for Combinatorial Drug Recommendation
• Authors: Chongmyung Kwon, Yujin Kim, Seoeun Park, Yunji Lee, Charmgil Hong,
• Abstract summary: Drug-drug interactions (DDI) between co-prescribed medications remain a significant challenge.<n>We propose Multimodal DDI Prediction with Molecular Electron Localization Function (ELF) Maps (MMM)<n>MMM combines ELF-derived features that encode global electronic properties with a bipartite graph encoder that models local substructure interactions.<n>These results demonstrate the potential complementary ELF-based 3D representations to enhance prediction accuracy and support safer drug prescribing in clinical practice.
• Score: 5.017879531498458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drug recommendation is an essential task in machine learning-based clinical decision support systems. However, the risk of drug-drug interactions (DDI) between co-prescribed medications remains a significant challenge. Previous studies have used graph neural networks (GNNs) to represent drug structures. Regardless, their simplified discrete forms cannot fully capture the molecular binding affinity and reactivity. Therefore, we propose Multimodal DDI Prediction with Molecular Electron Localization Function (ELF) Maps (MMM), a novel framework that integrates three-dimensional (3D) quantum-chemical information into drug representation learning. It generates 3D electron density maps using the ELF. To capture both therapeutic relevance and interaction risks, MMM combines ELF-derived features that encode global electronic properties with a bipartite graph encoder that models local substructure interactions. This design enables learning complementary characteristics of drug molecules. We evaluate MMM in the MIMIC-III dataset (250 drugs, 442 substructures), comparing it with several baseline models. In particular, a comparison with the GNN-based SafeDrug model demonstrates statistically significant improvements in the F1-score (p = 0.0387), Jaccard (p = 0.0112), and the DDI rate (p = 0.0386). These results demonstrate the potential of ELF-based 3D representations to enhance prediction accuracy and support safer combinatorial drug prescribing in clinical practice.

Related papers

• Pharmacology Knowledge Graphs: Do We Need Chemical Structure for Drug Repurposing? [0.0]
  We constructed a pharmacology knowledge graph from ChEMBL 36 comprising 5,348 entities including 3,127 drugs, 1,156 proteins, and 1,065 indications.<n>We benchmarked five knowledge graph embedding models and a standard graph neural network with 3.44 million parameters that incorporates drug chemical structure using a graph attention encoder and ESM-2 protein embeddings.<n>Removing the graph attention based drug structure encoder and retaining only topological embeddings combined with ESM-2 protein features improved drug protein PR-AUC from 0.5631 to 0.5785 while reducing VRAM usage from GB to 353 MB.
  arXiv  Detail & Related papers  (2026-03-02T07:07:32Z)
• A Hybrid Computational Intelligence Framework with Metaheuristic Optimization for Drug-Drug Interaction Prediction [0.8602553195689512]
  Drug-drug interactions (DDIs) are a leading cause of preventable adverse events, often complicating treatment and increasing healthcare costs.<n>We propose an interpretable and efficient framework that blends modern machine learning with domain knowledge to improve DDI prediction.<n>Our approach combines two complementary embeddings - Mol2Vec, which captures fragment-level structural patterns, and SMILES-BERT, which learns contextual chemical features.
  arXiv  Detail & Related papers  (2025-10-08T09:55:18Z)
• Aligned Manifold Property and Topology Point Clouds for Learning Molecular Properties [55.2480439325792]
  This work introduces AMPTCR, a molecular surface representation that combines local quantum-derived scalar fields and custom topological descriptors within an aligned point cloud format.<n>For molecular weight, results confirm that AMPTCR encodes physically meaningful data, with a validation R2 of 0.87.<n>In the bacterial inhibition task, AMPTCR enables both classification and direct regression of E. coli inhibition values.
  arXiv  Detail & Related papers  (2025-07-22T04:35:50Z)
• MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms [0.0]
  Drug combination therapies have shown promising therapeutic efficacy in complex diseases and have demonstrated the potential to reduce drug resistance.<n>The huge number of possible drug combinations makes it difficult to screen them all in traditional experiments.<n>In this study, we proposed MD-Syn, a computational framework, which is based on the multidimensional feature fusion method and multi-head attention mechanisms.
  arXiv  Detail & Related papers  (2025-01-14T06:50:56Z)
• BAPULM: Binding Affinity Prediction using Language Models [7.136205674624813]
  We introduce BAPULM, an innovative sequence-based framework that leverages the chemical latent representations of proteins via ProtT5-XL-U50 and through MolFormer. Our approach was validated extensively on benchmark datasets, achieving sequential scoring power (R) values of 0.925 $pm$ 0.043, 0.914 $pm$ 0.004, and 0.8132 $pm$ 0.001 on benchmark1k2101, Test2016_290, and CSAR-HiQ_36, respectively.
  arXiv  Detail & Related papers  (2024-11-06T04:35:30Z)
• Medication Recommendation via Dual Molecular Modalities and Multi-Step Enhancement [6.927266015351967]
  Existing works based on molecular knowledge neglect the 3D geometric structure of molecules and fail to learn the high-dimensional information of medications. We propose a bimodal molecular recommendation framework named BiMoRec, which introduces 3D molecular structures to obtain atomic 3D coordinates and edge indices.
  arXiv  Detail & Related papers  (2024-05-30T07:13:08Z)
• Regressor-free Molecule Generation to Support Drug Response Prediction [83.25894107956735]
  Conditional generation based on the target IC50 score can obtain a more effective sampling space. Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels.
  arXiv  Detail & Related papers  (2024-05-23T13:22:17Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy [2.446672595462589]
  We propose a multimodal graph neural network that can predict the synergistic effect of drug combinations for cancer treatment. Our model captures the representation based on the context of drugs at multiple scales based on a drug-protein interaction network and metadata. We demonstrate that the model makes high-quality predictions over a wide range of cancer cell line tissues.
  arXiv  Detail & Related papers  (2021-10-28T13:10:25Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.