ReviewJournal of xenobiotics2024
Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery.
Review in Journal of xenobiotics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Disrupting virulence: in silico discovery of MexL inhibitors to block pyocyanin biosynthesis in Pseudomonas aeruginosa.Archives of microbiology · 2026Article
- Integrating chemical structure and high-throughput transcriptomics for mechanistically interpretable Tox21 bioactivity prediction.Journal of cheminformatics · 2026Article
- Qualitative and quantitative in silico toxicity profiling of "angel dust": phencyclidine (PCP) analogues as new psychoactive substances (3-HO-PCP, 3-MeO-PCP, 4-MeO-PCP, 3-HO-PCE, 3-MeO-PCE, 4-MeO-PCE).Archives of toxicology · 2026Article
- Robustness Analyses Substantially Narrow in silico Network-Toxicology Signals Linking Selected Pharmaceuticals and Caffeine to Vitiligo.Clinical, cosmetic and investigational dermatology · 2026Article
- SM-GAT: a safety-aware multi-task graph attention network for multi-target anti-diabetic lead discovery from natural products.Frontiers in molecular biosciences · 2026Article
- Designing AI-generated antimicrobials for targeting bacterial microdomains.Scientific reports · 2025Article
- Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation.Current environmental health reports · 2025Review
- Integrative Assessment ofInternational journal of molecular sciences · 2025Article
- Speculation on the Mechanism of Parkinson's Disease Induced by Risk Residual Pesticides in Fresh Jujube and Hawthorn Through Network Toxicology and Molecular Docking Analysis.Foods (Basel, Switzerland) · 2025Article
- AI-Integrated QSAR Modeling for Enhanced Drug Discovery: From Classical Approaches to Deep Learning and Structural Insight.International journal of molecular sciences · 2025Review
- From molecules to data: the emerging impact of chemoinformatics in chemistry.Journal of cheminformatics · 2025Review
- Monte Carlo Simulation of Pesticide Toxicity for Rainbow Trout (Journal of xenobiotics · 2025Article
- Bridging science and curriculum: preparing future leaders in computational toxicology.Frontiers in toxicology · 2025Review
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
In the field of computational chemistry, computer models are quickly and cheaply constructed to predict toxicology hazards and results, with no need for test material or animals as these computational predictions are often based on physicochemical properties of chemical structures. Multiple methodologies are employed to support in silico assessments based on machine learning (ML) and deep learning (DL). This review introduces the development of computational toxicology, focusing on ML and DL and emphasizing their importance in the field of toxicology. A fine balance between target potency, selectivity, absorption, distribution, metabolism, excretion, toxicity (ADMET) and clinical safety properties should be achieved to discover a potential new drug. It is advantageous to perform virtual predictions as early as possible in drug development processes, even before a molecule is synthesized. Currently, there are numerous commercially available and free web-based programs for toxicity prediction, which can be used to construct various predictive models. The key features of the QSAR method are also outlined, and the selection of appropriate physicochemical descriptors is a prerequisite for robust predictions. In addition, examples of open-source tools applied to toxicity prediction are included, as well as examples of the application of different computational methods for the prediction of toxicity in drug design and environmental toxicology.
Indexed as
Identifiers
What Socratic 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 Socratic graph.