ArticlePharmaceuticals (Basel, Switzerland)2021
Cold-Start Problems in Data-Driven Prediction of Drug-Drug Interaction Effects.
Article in Pharmaceuticals (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature.Pharmaceutical medicine · 2022Pooled it
- Governing AI for Pharmacovigilance in Low-Income Countries: Systems Perspective.Journal of medical Internet research · 2026Article
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.BMC bioinformatics · 2025Article
- Fast polypharmacy side effect prediction using tensor factorization.Bioinformatics (Oxford, England) · 2024Article
- The future of medicine: an outline attempt using state-of-the-art business and scientific trends.Frontiers in medicine · 2024Review
- Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities.Frontiers in drug safety and regulation · 2024Review
- Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.Basic & clinical pharmacology & toxicology · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug-drug interaction. Predicting drug-drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug-drug interaction effects earlier during drug development.
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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 OpenQuestion graph.