ArticleJournal of cheminformatics2016
Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets.
Article in Journal of cheminformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Computational Discovery of Novel SGLT2 Inhibitors from Eight Selected Medicine Food Homology Herbs Using a Multi-Stage Virtual Screening Pipeline.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A comprehensive evaluation of advanced methods for identifying structural alerts using extensive toxicity data.Journal of cheminformatics · 2026Article
- Python tools for structural tasks in chemistry.Molecular diversity · 2025Review
- Machine learning: Python tools for studying biomolecules and drug design.Molecular diversity · 2025Review
- Scaffold and SAR studies on c-MET inhibitors using machine learning approaches.Journal of pharmaceutical analysis · 2025Article
- Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.International journal of molecular sciences · 2024Review
- Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction.Journal of cheminformatics · 2024Article
- Multiple machine learning methods aided virtual screening of NaJournal of cellular and molecular medicine · 2023Article
- MIFNN: Molecular Information Feature Extraction and Fusion Deep Neural Network for Screening Potential Drugs.Current issues in molecular biology · 2022Article
- In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities.BMC pharmacology & toxicology · 2021Article
- e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods.Frontiers in chemistry · 2018Article
- Review
- Open source molecular modeling.Journal of molecular graphics & modelling · 2016Review
- In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways.Wiley interdisciplinary reviews. Computational molecular scienceReview
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1 author.
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Abstract
backgroundAssessing compound toxicity at early stages of the drug discovery process is a crucial task to dismiss drug candidates likely to fail in clinical trials. Screening drug candidates against structural alerts, i.e. chemical fragments associated to a toxicological response prior or after being metabolized (bioactivation), has proved a valuable approach for this task. During the last decades, diverse algorithms have been proposed for the automatic derivation of structural alerts from categorical toxicity data sets. RESULTS AND
conclusionsHere, the python library bioalerts is presented, which comprises functionalities for the automatic derivation of structural alerts from categorical (dichotomous), e.g. toxic/non-toxic, and continuous bioactivity data sets, e.g. [Formula: see text] or [Formula: see text] values. The library bioalerts relies on the RDKit implementation of the circular Morgan fingerprint algorithm to compute chemical substructures, which are derived by considering radial atom neighbourhoods of increasing bond radius. In addition to the derivation of structural alerts, bioalerts provides functionalities for the calculation of unhashed (keyed) Morgan fingerprints, which can be used in predictive bioactivity modelling with the advantage of allowing for a chemically meaningful deconvolution of the chemical space. Finally, bioalerts provides functionalities for the easy visualization of the derived structural alerts.
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