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1. Drug-disease networks and drug repurposing

   https://doi.org/10.1371/journal.pcbi.1013595  

   Polanco, Austin; Newman, Mark_E J (October 2025, PLOS Computational Biology)

   Moreno, Yamir (Ed.)

   Repurposing existing drugs to treat new diseases is a cost-effective alternative tode novodrug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing textual and machine-readable databases, natural-language processing tools, and hand curation, and analyze it using network-based link prediction methods to identify potential drug-disease combinations. We measure the efficacy of these methods using cross-validation tests and find that several methods, particularly those based on graph embedding and network model fitting, achieve impressive prediction performance, significantly better than previous approaches, with area under the ROC curve above 0.95 and average precision almost a thousand times better than chance. 

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2. Joint Learning of Drug-Drug Combination and Drug-Drug Interaction via Coupled Tensor-Tensor Factorization with Side Information

   https://doi.org/10.64898/2026.02.13.705792  

   Zhang, Xiaoge; Fang, Zhengyu; Tang, Kaiyu; Chen, Huiyuan; Li, Jing (February 2026, bioRxiv, accepted by RECOMB2026)

   Abstract Targeted drug therapies offer a promising approach for treating complex diseases, with combinational drug therapies often employed to enhance therapeutic efficacy. However, unintended drug-drug interactions may undermine treatment outcomes or cause adverse side effects. In this work, we propose a novel joint learning framework for the simultaneous prediction of effective drug combinations and drug-drug interactions, based on coupled tensor-tensor factorization. Specifically, we model drug combination therapies and DDI by representing drug-drug-disease associations and drug-drug interaction profiles as coupled three-way tensors. To address the challenges of data incompleteness and sparsity, the proposed model integrates auxiliary drug similarity information, such as chemical structure similarities, drug-specific side effects, drug target profiles, and drug inhibition data on cancer cell lines, within a multi-view learning frame-work. For optimization, we adopt a modified Alternating Direction Method of Multipliers (ADMM) algorithm that ensures convergence while enforcing non-negativity constraints. In addition to standard tensor completion tasks, we further evaluate the proposed method under a more realisticnew-drug predictionsetting, where all interactions involving a previously unseen drug are withheld. This scenario closely aligns with real-world applications, in which reliable predictions for emerging or under-studied compounds are essential. We evaluate the proposed method on a comprehensive dataset compiled from multiple sources, including DrugBank, CDCDB, SIDER, and PubChem. Our experiments show that SI-ADMM maintains robust performance and achieves the best results comparing to other tensor factorization approaches, with or without auxiliary information, particularly in the new-drug prediction setting. The implementation of our method is publicly available at:https://github.com/Xiaoge-Zhang/SI-ADMM. 

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3. HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network

   https://doi.org/10.1109/ICDE55515.2023.00119  

   Saifuddin, Khaled Mohammed; Bumgardner, Briana; Tanvir, Farhan; Akbas, Esra (April 2023, 2023 IEEE 39th International Conference on Data Engineering (ICDE))
4. PartialFibers: An efficient method for predicting Drug-Drug Interactions

   Bumin, Aysegul; Huang, Kejun; Kahveci, Tamer (December 2023, Springer)

   Drug resistance is one of the fundamental challenges in modern medicine. Using combinations of drugs is an effective solution to counter drug resistance as is harder to develop resistance to multiple drugs simultaneously. Finding the correct dosage for each drug in the combination remains to be a challenging task. Testing all possible drug-drug combinations on various cell lines for different dosages in wet-lab experiments is infeasible since there are many combinations of drugs as well as their dosages yet the drugs and the cell lines are limited in availability and each wet-lab test is costly and time-consuming. Efficient and accurate in silico prediction methods are surely needed. Here we present a novel computational method, PartialFibers to address this challenge. Unlike existing prediction methods PartialFibers takes advantage of the distribution of the missing drug-drug interactions and effectively predicts the dosage of a drug in the combination. Our results on real datasets demonstrate that PartialFibers is more flexible, scalable, and achieves higher accuracy in less time than the state of the art algorithms. 

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5. Detecting Drug-Drug Interactions using Protein Sequence-Structure Similarity Networks

   https://doi.org/10.1109/BIBM52615.2021.9669858  

   Islam, Saminur; Abbasi, Ahmed; Agarwal, Nitin; Zheng, Wanhong; Doretto, Gianfranco; Adjeroh, Donald A. (December 2021, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))