ArticleInternational journal of molecular sciences2022
iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- DeepADR: multimodal prediction of adverse drug reaction frequency by integrating early-stage drug discovery information via Kolmogorov-Arnold networks.Briefings in bioinformatics · 2025Article
- MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction.Scientific reports · 2025Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- A machine learning method for predicting molecular antimicrobial activity.Scientific reports · 2025Article
- Predicting Drugs Suspected of Causing Adverse Drug Reactions Using Graph Features and Attention Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Adverse drug reactions (ADRs) are a major issue to be addressed by the pharmaceutical industry. Early and accurate detection of potential ADRs contributes to enhancing drug safety and reducing financial expenses. The majority of the approaches that have been employed to identify ADRs are limited to determining whether a drug exhibits an ADR, rather than identifying the exact type of ADR. By introducing the "multi-level feature-fusion deep-learning model", a new predictor, called iADRGSE, has been developed, which can be used to identify adverse drug reactions at the early stage of drug discovery. iADRGSE integrates a self-attentive module and a graph-network module that can extract one-dimensional sub-structure sequence information and two-dimensional chemical-structure graph information of drug molecules. As a demonstration, cross-validation and independent testing were performed with iADRGSE on a dataset of ADRs classified into 27 categories, based on SOC (system organ classification). In addition, experiments comparing iADRGSE with approaches such as NPF were conducted on the OMOP dataset, using the jackknife test method. Experiments show that iADRGSE was superior to existing state-of-the-art predictors.
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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.