ArticleScientific reports2017
Predicting neurological Adverse Drug Reactions based on biological, chemical and phenotypic properties of drugs using machine learning models.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Machine learning models for adverse drug reaction prediction. A systematic review and three-level multilevel meta-analysis.Frontiers in pharmacology · 2026Pooled it
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Computational Prediction of the Severity of Adverse Drug Reactions Caused by Drug-Drug Interactions.Pharmaceuticals (Basel, Switzerland) · 2026Article
- DeepADR: multimodal prediction of adverse drug reaction frequency by integrating early-stage drug discovery information via Kolmogorov-Arnold networks.Briefings in bioinformatics · 2025Article
- New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.Acta pharmaceutica Sinica. B · 2025Review
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data.Scientific reports · 2024Article
- Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints.Molecular diversity · 2024Article
- Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings.Scientific reports · 2024Article
- Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2024Article
- SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.Scientific reports · 2023Article
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
- Multimodal representation learning for predicting molecule-disease relations.Bioinformatics (Oxford, England) · 2023Article
- Integrative analysis of chemical properties and functions of drugs for adverse drug reaction prediction based on multi-label deep neural network.Journal of integrative bioinformatics · 2022Article
- Brain physiome: A concept bridgingBioactive materials · 2022Review
- Current status and future directions for a neurotoxicity hazard assessment framework that integratesComputational toxicology (Amsterdam, Netherlands) · 2022Article
- Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects.Computational and mathematical methods in medicine · 2022Article
- Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.Scientific reports · 2021Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Prediction of adverse drug reactions based on knowledge graph embedding.BMC medical informatics and decision making · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adverse drug reactions (ADRs) have become one of the primary reasons for the failure of drugs and a leading cause of deaths. Owing to the severe effects of ADRs, there is an urgent need for the generation of effective models which can accurately predict ADRs during early stages of drug development based on integration of various features of drugs. In the current study, we have focused on neurological ADRs and have used various properties of drugs that include biological properties (targets, transporters and enzymes), chemical properties (substructure fingerprints), phenotypic properties (side effects (SE) and therapeutic indications) and a combinations of the two and three levels of features. We employed relief-based feature selection technique to identify relevant properties and used machine learning approach to generated learned model systems which would predict neurological ADRs prior to preclinical testing. Additionally, in order to explain the efficiency and applicability of the models, we tested them to predict the ADRs for already existing anti-Alzheimer drugs and uncharacterized drugs, respectively in side effect resource (SIDER) database. The generated models were highly accurate and our results showed that the models based on chemical (accuracy 93.20%), phenotypic (accuracy 92.41%) and combination of three properties (accuracy 94.18%) were highly accurate while the models based on biological properties (accuracy 82.11%) were highly informative.
Indexed as
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.