Drug-Drug Interaction Prediction

Latest papers

15 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

MoEDDI: Semantic Mixtures of Experts for Long Tailed Drug Drug Interaction Prediction
Oct 31, 2026Dinh Tan Nguyen, Hien Quang Kha, Minh-Toan Dinh +5Drug-Drug Interaction Prediction Mixture-of-Experts Models
University of Technology Sydney, Australia · Taipei Medical University, Taiwan · National Central University, Taiwan +4
PRISMATIC : Prescription Risk Inspection System for Multi-Agent Treatment Interaction in Clinical Decision-Making
Oct 14, 2026Ziyang Ling, Shuyu Lu, Ziyi ChenDrug-Drug Interaction Prediction Clinical Decision Support
University of Science and Technology of China, Hefei, China · The University of Texas Health Science Center at Houston, Houston, United States
ColdDDI: Evaluating Knowledge Utilization in Cold-Start Drug-Drug Interaction Prediction
Oct 4, 2026Jiheng Liang, Chen Zhao, Di Wu +4Knowledge Retrieval in Language Models Drug-Drug Interaction Prediction
Data Science, William & Mary · Computer Science, Baylor University · Southwest University +2
VINCENT: Validated Interaction Network for Cross-drug Explanation of Therapeutics
Aug 26, 2026Fan-Sheng Chuang, Xuchen Li, Yujing Bian +1Drug Discovery Drug-Drug Interaction Prediction
North Carolina State University Computer Science Raleigh, USA · North Carolina State University Electrical and Computer Engineering Raleigh, USA
GRAIN: Molecules Are Not the Right Granularity -- Active-Ingredient Modeling for Safe Medication Recommendation
Jul 30, 2026Juao Fan, Jinhan Li, Shengxin ZhuMedication Recommendation Clinical Decision Support
Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Beijing Normal–Hong Kong Baptist University, Zhuhai, China
Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions
Jul 8, 2026Faranak Hatami, Mousa MoradiClass-Imbalanced Learning Graph Neural Networks
Department of Chemistry, University of Illinois Chicago, Chicago, IL 60607, USA · Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
CoFEND: A Cross-Modal Fusion End-to-End Network for Cold-Start Drug-Drug Interaction Prediction
Jul 3, 2026Di Wu, Hongyi Sun, Haichao Xu +3Multimodal Graph Learning Drug Discovery
School of Physics and Electronic Science, Zunyi Normal University
DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction
Jun 30, 2026Zhenqian Shen, Yu Liu, Xiaoyi Fu +1Multi-Agent LLM Systems AI for Science
Department of Electronic Engineering, Tsinghua University Beijing, China · Institute of Biomedical Engineering, University of Oxford Oxford, United Kingdom · Division of Emerging Interdisciplinary Areas, Hong Kong University of Science and Technology Hong Kong, China
MARD: Mirror-Augmented Reasoning Distillation for Mechanism-Level Drug-Drug Interaction Prediction
Jun 10, 2026Mohammadreza Riyazat, Vian Lelo, Rameen Jafri +2Knowledge Distillation Drug Discovery
University of Guelph, Canada · York University, Canada · Vector Institute, Canada
Pharmacogenomic Knowledge Graph Augmentation for Graph Neural Network-Based Drug-Drug Interaction Prediction
Jun 5, 2026Juergen DietrichGraph Neural Networks Drug-Drug Interaction Prediction
AI Solutions Berlin
SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation
May 27, 2026Xinyu Wang, Hanwei Wu, Zhenghan Tai +7Medication Recommendation Multi-Agent LLM Systems
McGill University · McMaster University · University of Toronto +1
From Detection to Mechanism: Cross-Attention Graph Neural Networks Enable Drug-Drug Interaction Type Prediction An Ablation Study with Acetylsalicylic Acid Validation
May 27, 2026Juergen DietrichGraph Neural Networks Drug-Drug Interaction Prediction
AIM-DDI: A Model-Agnostic Multimodal Integration Module for Drug-Drug Interaction Prediction
May 14, 2026Yerin Park, Sangseon LeeCross-Modal Representation Learning Drug-Drug Interaction Prediction
Department of Artificial Intelligence, Inha University
Enhanced Drug-drug Interaction Prediction Using Adaptive Knowledge Integration
Mar 13, 2026Pengfei Liu, Jun Tao, Zhixiang RenDrug-Drug Interaction Prediction Knowledge Augmentation for Language Models
Pengcheng Laboratory, Shenzhen 518000 · School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Multimodal AI predicts clinical outcomes of drug combinations from preclinical data
Mar 4, 2025Yepeng Huang, Xiaorui Su, Varun Ullanat +10Clinical Outcome Prediction Multimodal Learning
Department of Biomedical Informatics, Harvard Medical School, Boston, MA · Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA · Harvard College, Cambridge, MA +8

Oct 31, 2026J/K move · Enter open · S save

MoEDDI: Semantic Mixtures of Experts for Long Tailed Drug Drug Interaction Prediction

Dinh Tan Nguyen, Hien Quang Kha, Minh-Toan Dinh, Xuan-Huy Nguyen+4

University of Technology Sydney, Australia · Taipei Medical University, Taiwan · National Central University, Taiwan+4

Drug-Drug Interaction Prediction Mixture-of-Experts Models Mixture-of-Experts Expert Specialization Long-Tailed Classification

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Descriptor-based drug--drug interaction (DDI) models typically apply a single global predictor despite heterogeneous physicochemical evidence and severely long-tailed labels. We introduce MoEDDI (Mixture-of-Experts for Drug--Drug Interaction prediction), a chemically organized mixture-of-experts that routes overlapping descriptor families using low-dimensional statistics and combines their sparse top-

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mixture with a global T-DDI pathway through a residual logit correction. The residual parameterization can exactly preserve a supplied T-DDI checkpoint at initialization, although the evaluated models are trained from scratch. Across complete test partitions with 178, 86, and 92 classes, MoEDDI improves macro-F1 over reproduced seed-2026 T-DDI baselines from 0.8284 to 0.8485, 0.4729 to 0.6536, and 0.1807 to 0.4671. On the 3,780-feature corpus, the lowest-support group (89 classes with fewer than 50 test examples) shows the largest mean classwise gain (+3.30 F1 points). Calibration improves on one corpus but worsens on two. These single-seed results support semantic conditional specialization while motivating matched ablations and distribution-shift evaluation.