ArticleFrontiers in chemistry2022
SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.
Article in Frontiers in chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- ChronicDPipredictor: an interpretable deep learning framework for chemical chronic and subchronic toxicity assessment.Molecular diversity · 2026Article
- In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.Molecular diversity · 2026Article
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- A comprehensive evaluation of advanced methods for identifying structural alerts using extensive toxicity data.Journal of cheminformatics · 2026Article
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Evaluating the consistency of judgments derived through both in silico and expert application of the Cramer classification scheme.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2024Article
- Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.ACS chemical neuroscience · 2024Article
- Recent advances and current challenges of new approach methodologies in developmental and adult neurotoxicity testing.Archives of toxicology · 2024Review
- Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity.ACS omega · 2023Article
- Modeling and insights into the structural characteristics of drug-induced autoimmune diseases.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be "black box", which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.
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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.