ReviewInternational journal of molecular sciences2018
Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.
Review in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 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.
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
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis.International journal of molecular sciences · 2024Pooled it
- Molecular Mechanisms of Acute Drug Toxicity in Polypharmacy: Analgesic-Psychotropic Interactions.International journal of molecular sciences · 2026Review
- Natural chlocarbazomycins as potential adenosine A1 receptor antagonists: ligand-based and structure-based virtual screening, quantum chemical analysis and CNS MPO study.Molecular diversity · 2026Article
- Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Learning Multidimensional Electronegativity of Selected Atom Types in Organic Molecules Using Graph Neural Networks.ACS omega · 2025Article
- Article
- A robust and statistical analyzed predictive model for drug toxicity using machine learning.Scientific reports · 2025Article
- An Imbalance Regression Approach to Toxicity Prediction of Chemicals for Potential Use in Environmentally Acceptable Lubricants.ACS applied materials & interfaces · 2025Article
- Hepatotoxicity Evaluation of Levornidazole and Its Three Main Impurities: Based on Structure-Toxicity Classification Prediction Combined with Zebrafish Toxicity Assessment.Molecules (Basel, Switzerland) · 2025Article
- A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network.Frontiers in toxicology · 2025Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach.NPJ digital medicine · 2024Article
- Towards a comprehensive assessment of QSP models: what would it take?Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Integration of machine learning models with microsatellite markers: New avenue in world grapevine germplasm characterization.Biochemistry and biophysics reports · 2024Article
- Thyroid Hormone Biomonitoring: A Review on Their Metabolism and Machine-Learning Based Analysis on Effects of Endocrine Disrupting Chemicals.Environment & health (Washington, D.C.) · 2024Review
- The Future of Material Scientists in an Age of Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.Nature communications · 2024Article
- Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity.ACS omega · 2024Article
- Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine).Archives of toxicology · 2024Review
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
Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.
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.