ArticleBioinformatics advances2023
DPSP: a multimodal deep learning framework for polypharmacy side effects prediction.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
30 citing papers in PubMed.
- Mol2Image: an enhanced DDI prediction framework leveraging drug molecular descriptors.BMC bioinformatics · 2026Article
- RLASON-CDR: a reinforcement learning-driven adaptive synergistic optimization network for cancer drug response prediction.Briefings in bioinformatics · 2026Article
- Machine learning and deep learning-based drug-drug interactions prediction: a systematic review focused on anticancer drugs.NPJ precision oncology · 2026Article
- LKE-DTA: predicting drug-target binding affinity with large language model representations and knowledge graph embeddings.Molecular diversity · 2026Article
- PDTSyn: a parameter-decomposed transformer for domain-generalized cell line-aware drug synergy prediction.Briefings in bioinformatics · 2026Article
- MFCN-DDI: Capsule network based on multimodal feature for multitype drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2026Article
- Advancing Drug-Drug Interaction Prediction with Biomimetic Improvements: Leveraging the Latest Artificial Intelligence Techniques to Guide Researchers in the Field.Biomimetics (Basel, Switzerland) · 2026Review
- Dual-protein embedding-based graph model with dynamic attention for interaction prediction.Briefings in bioinformatics · 2025Article
- A multi-dimensional computational framework of drug-induced hepatotoxicity: integrating molecular structure features with disease pathogenesis.Briefings in bioinformatics · 2025Article
- BioWalk-MDA: a novel approach for large-scale predicting metabolite-drug associations based on multi layered biomedical knowledge graphs.Briefings in bioinformatics · 2025Article
- Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET.Briefings in bioinformatics · 2025Article
- Drug repurposing for Alzheimer's disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph.BioData mining · 2025Article
- MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction.Scientific reports · 2025Article
- A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.Bioinformatics (Oxford, England) · 2025Article
- Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach.Journal of integrative bioinformatics · 2025Article
- Systematic identification of cancer-type-specific drugs based on essential genes and validations in lung adenocarcinoma.Briefings in bioinformatics · 2025Article
- Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction.Briefings in bioinformatics · 2025Article
- PathSynergy: a deep learning model for predicting drug synergy in liver cancer.Briefings in bioinformatics · 2025Article
- Relational similarity-based graph contrastive learning for DTI prediction.Briefings in bioinformatics · 2025Article
- PolyLLM: polypharmacy side effect prediction via LLM-based SMILES encodings.Frontiers in pharmacology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Because unanticipated drug-drug interactions (DDIs) can result in severe bodily harm, identifying the adverse effects of polypharmacy is one of the most important tasks in human health. Over the past few decades, computational methods for predicting the adverse effects of polypharmacy have been developed. Results: This article presents DPSP, a framework for predicting polypharmacy side effects based on the construction of novel drug features and the application of a deep neural network to predict DDIs. In the first step, a variety of drug information is evaluated, and a feature extraction method and the Jaccard similarity are used to determine similarities between two drugs. By combining these similarities, a novel feature vector is generated for each drug. In the second step, the method predicts DDIs for specific DDI events using a multimodal framework and drug feature vectors. On three benchmark datasets, the performance of DPSP is measured by comparing its results to those of several well-known methods, such as GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, DNN, DeepDDI, KNN, LR, and RF. DPSP outperforms these classification methods based on a variety of classification metrics. The results indicate that the use of diverse drug information is effective and efficient for identifying DDI adverse effects. Availability and implementation: The source code and datasets are available at https://github.com/raziyehmasumshah/DPSP.
Identifiers
What OpenQuestion holds
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.