ArticleScientific reports2019
Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Features Forecasting Patient Arrival Numbers.Computers, informatics, nursing : CIN · 2025Pooled it
- New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Advancing Drug-Drug Interaction Prediction with Biomimetic Improvements: Leveraging the Latest Artificial Intelligence Techniques to Guide Researchers in the Field.Biomimetics (Basel, Switzerland) · 2026Review
- Research on drug-drug interaction prediction using capsule neural network based on self-attention mechanism.BMC bioinformatics · 2025Article
- PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.Scientific reports · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction.Journal of pharmaceutical analysis · 2025Article
- Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges.Frontiers in pharmacology · 2025Review
- Predicting the toxic side effects of drug interactions using chemical structures and protein sequences.Scientific reports · 2024Article
- Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction.Mayo Clinic proceedings. Digital health · 2024Article
- Accelerating adverse pregnancy outcomes research amidst rising medication use: parallel retrospective cohort analyses for signal prioritization.BMC medicine · 2024Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Sex as a Biological Variable in Early-Phase Oncology Clinical Trials: Enhancing the Path to Personalised Medicine.Heliyon · 2024Review
- Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.Journal of translational medicine · 2024Article
- DDI-Transform: A neural network for predicting drug-drug interaction events.Quantitative biology (Beijing, China) · 2024Article
- DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.NPJ systems biology and applications · 2024Article
- Deep learning for drug-drug interaction prediction: A comprehensive review.Quantitative biology (Beijing, China) · 2024Review
- Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.Scientific reports · 2024Article
- Learning self-supervised molecular representations for drug-drug interaction prediction.BMC bioinformatics · 2024Article
- Toward Unified AI Drug Discovery with Multimodal Knowledge.Health data science · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD .
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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.