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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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This content will become publicly available on October 16, 2026
Drug-disease networks and drug repurposing
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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- PAR ID:
- 10700206
- Editor(s):
- Moreno, Yamir
- Publisher / Repository:
- Public Library of Science
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 21
- Issue:
- 10
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1013595
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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