ReviewToxicological research2024
Efficiency of pharmaceutical toxicity prediction in computational toxicology.
Review in Toxicological research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Pediatric bronchial ulcers induced by ibuprofen overdose: a case report combined with network toxicology to decipher potential mechanisms.Frontiers in pharmacology · 2026Article
- Exploring the Androgen Receptor Binding Affinity of Azole Derivatives through Multiscale Computational and Experimental Approaches.Environmental science & technology · 2025Article
- Coumarins fromOncoTargets and therapy · 2025Article
- Predicting non-chemotherapy drug-induced agranulocytosis toxicity through ensemble machine learning approaches.Frontiers in pharmacology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The adverse effects and toxicity of chemical substances pose substantial challenges in drug discovery and environmental science. Their management, most especially in the early development stage, is crucial in preventing costly failures in clinical trials. Predictive methodologies, such as computational toxicology, offer an effective means of managing risks, particularly for new compounds with insufficient post-marketing surveillance and those lacking information on adverse effects. Computational approaches have become increasingly important in environmental science, in which the sheer number and diversity of chemicals present similar challenges to toxicity control. Traditional animal-based evaluation methods are resource intensive, time consuming, and ethically problematic, making them unsuitable for use in assessing the vast compound range. It is an urgent task for the academic community to minimize the risks associated with drug discovery and environmental exposure. This study focuses on systems used to predict toxicity from chemical structure information and outlines the prediction accuracy and systems developed in Japan.
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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.