ReviewJournal of computer-aided molecular design
In silico prediction of drug toxicity.
Review in Journal of computer-aided molecular design. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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
47 citing papers in PubMed.
- Dynamics-enhanced molecular property prediction guided by deep learning.PLoS computational biology · 2026Article
- Article
- Hydrogels from Protein-Polymer Conjugates: A Pathway to Next-Generation Biomaterials.Gels (Basel, Switzerland) · 2025Review
- Computational approaches to predict the toxicity of bioactive natural products: a mini review of methodologies.Food science and biotechnology · 2025Review
- In-silico identification and exploration of small molecule coumarin-1,2,3-triazole hybrids as potential EGFR inhibitors for targeting lung cancer.Molecular diversity · 2024Article
- New apoptotic anti-triple-negative breast cancer theobromine derivative inhibiting EGFRWT and EGFRMolecular diversity · 2024Article
- New Thieno[2,3-d]pyrimidines as Anticancer VEGFR-2 Inhibitors with Apoptosis Induction: Design, Synthesis, and Biological andMedicinal chemistry (Shariqah (United Arab Emirates)) · 2024Article
- Design, synthesis, docking, MD simulations, and anti-proliferative evaluation of thieno[2,3-Journal of enzyme inhibition and medicinal chemistry · 2023Article
- Ayurveda and in silico Approach: A Challenging Proficient Confluence for Better Development of Effective Traditional Medicine Spotlighting Network Pharmacology.Chinese journal of integrative medicine · 2023Review
- A New Anticancer Semisynthetic Theobromine Derivative Targeting EGFR Protein: CADDD Study.Life (Basel, Switzerland) · 2023Article
- Anticancer derivative of the natural alkaloid, theobromine, inhibiting EGFR protein: Computer-aided drug discovery approach.PloS one · 2023Article
- In-silico identification of small molecule benzofuran-1,2,3-triazole hybrids as potential inhibitors targeting EGFR in lung cancer via ligand-based pharmacophore modeling and molecular docking studies.In silico pharmacology · 2023Article
- (Molecules (Basel, Switzerland) · 2022Article
- New Anticancer Theobromine Derivative Targeting EGFRMolecules (Basel, Switzerland) · 2022Article
- DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.Interdisciplinary sciences, computational life sciences · 2022Article
- In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Screening of anti-Royal Society open science · 2021Article
- A history of the roles of cytochrome P450 enzymes in the toxicity of drugs.Toxicological research · 2021Review
- Design and synthesis of thiadiazolo-carboxamide bridged β-carboline-indole hybrids: DNA intercalative topo-IIα inhibition with promising antiproliferative activity.Bioorganic chemistry · 2020Article
- Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.Drug discovery today · 2020Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of these. A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints. Those discussed include TOPKAT, CASE, DEREK, HazardExpert, OncoLogic and COMPACT. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled.
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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.