ArticleBMC bioinformatics2021
A neural network-based method for polypharmacy side effects prediction.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 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
- LKE-DTA: predicting drug-target binding affinity with large language model representations and knowledge graph embeddings.Molecular diversity · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Dual-protein embedding-based graph model with dynamic attention for interaction prediction.Briefings in bioinformatics · 2025Article
- Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET.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
- A multi-dimensional computational framework of drug-induced hepatotoxicity: integrating molecular structure features with disease pathogenesis.Briefings in bioinformatics · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction.Scientific reports · 2025Article
- DrIVeNN: Drug Interaction Vectors Neural Network.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- medicX-KG: a knowledge graph for pharmacists' drug information needs.Journal of biomedical semantics · 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
- Reverse game: from Nash equilibrium to network structure, number and probability of occurrence.Royal Society open science · 2025Article
- Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction.Briefings in bioinformatics · 2025Article
- Systematic identification of cancer-type-specific drugs based on essential genes and validations in lung adenocarcinoma.Briefings in bioinformatics · 2025Article
- A novel deep sequential learning architecture for drug drug interaction prediction using DDINet.Scientific reports · 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
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3 authors.
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Abstract
backgroundPolypharmacy is a type of treatment that involves the concurrent use of multiple medications. Drugs may interact when they are used simultaneously. So, understanding and mitigating polypharmacy side effects are critical for patient safety and health. Since the known polypharmacy side effects are rare and they are not detected in clinical trials, computational methods are developed to model polypharmacy side effects.
resultsWe propose a neural network-based method for polypharmacy side effects prediction (NNPS) by using novel feature vectors based on mono side effects, and drug-protein interaction information. The proposed method is fast and efficient which allows the investigation of large numbers of polypharmacy side effects. Our novelty is defining new feature vectors for drugs and combining them with a neural network architecture to apply for the context of polypharmacy side effects prediction. We compare NNPS on a benchmark dataset to predict 964 polypharmacy side effects against 5 well-established methods and show that NNPS achieves better results than the results of all 5 methods in terms of accuracy, complexity, and running time speed. NNPS outperforms about 9.2% in Area Under the Receiver-Operating Characteristic, 12.8% in Area Under the Precision-Recall Curve, 8.6% in F-score, 10.3% in Accuracy, and 18.7% in Matthews Correlation Coefficient with 5-fold cross-validation against the best algorithm among other well-established methods (Decagon method). Also, the running time of the Decagon method which is 15 days for one fold of cross-validation is reduced to 8 h by the NNPS method.
conclusionsThe performance of NNPS is benchmarked against 5 well-known methods, Decagon, Concatenated drug features, Deep Walk, DEDICOM, and RESCAL, for 964 polypharmacy side effects. We adopt the 5-fold cross-validation for 50 iterations and use the average of the results to assess the performance of the NNPS method. The evaluation of the NNPS against five well-known methods, in terms of accuracy, complexity, and running time speed shows the performance of the presented method for an essential and challenging problem in pharmacology. Datasets and code for NNPS algorithm are freely accessible at https://github.com/raziyehmasumshah/NNPS .
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