ArticleSynthetic and systems biotechnology2019
Synergistic drug combinations prediction by integrating pharmacological data.
Article in Synthetic and systems biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.Research square · 2026Article
- A directed weighted network-based method for drug combinations identification using drug-target and inter-target regulation.BMC bioinformatics · 2025Article
- DeepTraSynergy: drug combinations using multimodal deep learning with transformers.Bioinformatics (Oxford, England) · 2023Article
- Machine learning methods, databases and tools for drug combination prediction.Briefings in bioinformatics · 2022Article
- Differential tissue distribution of discrete typing units after drug combination therapy in experimentalParasitology · 2021Article
- Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.PLoS computational biology · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy.NPJ systems biology and applications · 2021Article
- Anticancer drug synergy prediction in understudied tissues using transfer learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Folic acid-sulfonamide conjugates as antibacterial agents: design, synthesis and molecular docking studies.RSC advances · 2020Article
- Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy.Biology · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.