The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
ProTox-II: a webserver for the prediction of toxicity of chemicals
Open accessAbstract
Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
DeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081
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Computational methods for prediction of in vitro effects of new chemical structures
Banerjee, P., Siramshetty, V. B., Drwal, M. N., & Preissner, R. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0162-2 · Cited by 60
Physiological and behavioural responsivity to stress and anxiogenic stimuli in COMT-deficient mice
Desbonnet, L., Tighe, O., Karayiorgou, M., Gogos, J. A., Waddington, J. L., & O’Tuathaigh, C. M. P. (2012) · Behavioural Brain Research · doi:10.1016/j.bbr.2011.12.014 · Cited by 38
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
Opo, F. A. D. M., Rahman, M. M., Ahammad, F., Ahmed, I., Bhuiyan, M. A., & Asiri, A. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-83626-x · Cited by 298
Toxicity prediction based on artificial intelligence: A multidisciplinary overview
Pérez Santín, E., Rodríguez Solana, R., González García, M., García Suárez, M. D. M., Blanco Díaz, G. D., Cima Cabal, M. D., Moreno Rojas, J. M., & López Sánchez, J. I. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1516 · Cited by 153
Issahaku, A. R., Mukelabai, N., Agoni, C., Rudrapal, M., Aldosari, S. M., Almalki, S. G., & Khan, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-22668-1 · Cited by 93
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Metadata
- Authors
- Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-04-30
- Volume
- 46
- Issue
- W1
- Pages
- W257-W263
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2801088198
- Cited by
- 3,195
- References
- 5
- FWCI
- 65.69
- Type
- journal article
- Language
- en
- Open access
- gold
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- Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research. https://doi.org/10.1093/nar/gky318
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