SuperToxic: a Comprehensive Database of Toxic Compounds
Overview
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Within our everyday life, we are confronted with a variety of toxic substances of natural or artificial origin. Toxins are already used, e.g. in medicine, but there is still an increasing number of toxic compounds, representing a tremendous potential to extract new substances. Since predictive toxicology gains in importance, the careful and extensive investigation of known toxins is the basis to assess the properties of unknown substances. In order to achieve this aim, we have collected toxic compounds from literature and web sources in the database SuperToxic. The current version of this database compiles about 60,000 compounds and their structures. These molecules are classified according to their toxicity, based on more than 2 million measurements. The SuperToxic database provides a variety of search options like name, CASRN, molecular weight and measured values of toxicity. With the aid of implemented similarity searches, information about possible biological interactions can be gained. Furthermore, connections to the Protein Data Bank, UniProt and the KEGG database are available, to allow the identification of targets and those pathways, the searched compounds are involved in. This database is available online at: http://bioinformatics.charite.de/supertoxic.
Overview and limitations of database in global traditional medicines: A narrative review.
Li X, Zhang J, Shen X, Zhang Y, Guo D Acta Pharmacol Sin. 2024; 46(2):235-263.
PMID: 39095509 PMC: 11747326. DOI: 10.1038/s41401-024-01353-1.
Phytochemical Volatiles as Potential Bionematicides with Safer Ecotoxicological Properties.
Cavaco T, Faria J Toxics. 2024; 12(6).
PMID: 38922086 PMC: 11209200. DOI: 10.3390/toxics12060406.
ProTox 3.0: a webserver for the prediction of toxicity of chemicals.
Banerjee P, Kemmler E, Dunkel M, Preissner R Nucleic Acids Res. 2024; 52(W1):W513-W520.
PMID: 38647086 PMC: 11223834. DOI: 10.1093/nar/gkae303.
Mir S, Meher R, Nayak B Biochem Biophys Rep. 2023; 34:101459.
PMID: 36987522 PMC: 10037929. DOI: 10.1016/j.bbrep.2023.101459.
Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point.
Cavasotto C, Scardino V ACS Omega. 2023; 7(51):47536-47546.
PMID: 36591139 PMC: 9798519. DOI: 10.1021/acsomega.2c05693.