Molecular similarity-based predictions of the Tox21 screening outcome
To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative met...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-07-01
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| Series: | Frontiers in Environmental Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fenvs.2015.00054/full |
| _version_ | 1811238574360625152 |
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| author | Malgorzata Natalia Drwal Vishal Babu Siramshetty Priyanka eBanerjee Priyanka eBanerjee Andrean eGoede Robert ePreissner Robert ePreissner Robert ePreissner Mathias eDunkel |
| author_facet | Malgorzata Natalia Drwal Vishal Babu Siramshetty Priyanka eBanerjee Priyanka eBanerjee Andrean eGoede Robert ePreissner Robert ePreissner Robert ePreissner Mathias eDunkel |
| author_sort | Malgorzata Natalia Drwal |
| collection | DOAJ |
| description | To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modelling general and complex endpoints to the investigation and modelling of pathways of toxicity and the underlying molecular effects.The U.S. Toxicology in the 21st Century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models.Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naïve Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modelled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction. |
| first_indexed | 2024-04-12T12:45:08Z |
| format | Article |
| id | doaj.art-7cc91d374ecc4cfe82136501078c855e |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-04-12T12:45:08Z |
| publishDate | 2015-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-7cc91d374ecc4cfe82136501078c855e2022-12-22T03:32:39ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2015-07-01310.3389/fenvs.2015.00054150735Molecular similarity-based predictions of the Tox21 screening outcomeMalgorzata Natalia Drwal0Vishal Babu Siramshetty1Priyanka eBanerjee2Priyanka eBanerjee3Andrean eGoede4Robert ePreissner5Robert ePreissner6Robert ePreissner7Mathias eDunkel8Charité - University Medicine BerlinCharité - University Medicine BerlinCharité - University Medicine BerlinHumboldt-Universität zu BerlinCharité - University Medicine BerlinCharité - University Medicine BerlinFreie Universität BerlinGerman Cancer Consortium (DKTK)Charité - University Medicine BerlinTo assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modelling general and complex endpoints to the investigation and modelling of pathways of toxicity and the underlying molecular effects.The U.S. Toxicology in the 21st Century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models.Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naïve Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modelled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction.http://journal.frontiersin.org/Journal/10.3389/fenvs.2015.00054/fullmachine learningmolecular similarityMolecular fingerprintsToxicity predictionTox21 Data Challenge 2014 |
| spellingShingle | Malgorzata Natalia Drwal Vishal Babu Siramshetty Priyanka eBanerjee Priyanka eBanerjee Andrean eGoede Robert ePreissner Robert ePreissner Robert ePreissner Mathias eDunkel Molecular similarity-based predictions of the Tox21 screening outcome Frontiers in Environmental Science machine learning molecular similarity Molecular fingerprints Toxicity prediction Tox21 Data Challenge 2014 |
| title | Molecular similarity-based predictions of the Tox21 screening outcome |
| title_full | Molecular similarity-based predictions of the Tox21 screening outcome |
| title_fullStr | Molecular similarity-based predictions of the Tox21 screening outcome |
| title_full_unstemmed | Molecular similarity-based predictions of the Tox21 screening outcome |
| title_short | Molecular similarity-based predictions of the Tox21 screening outcome |
| title_sort | molecular similarity based predictions of the tox21 screening outcome |
| topic | machine learning molecular similarity Molecular fingerprints Toxicity prediction Tox21 Data Challenge 2014 |
| url | http://journal.frontiersin.org/Journal/10.3389/fenvs.2015.00054/full |
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