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DeepTox: Toxicity Prediction using Deep Learning
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The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 different toxic effects by specifically designed assays. We participated in this challenge to assess the performance of Deep Learning in computational toxicity prediction. Deep Learning has already revolutionized image processing, speech recognition, and language understanding but has not yet been applied to computational toxicity. Deep Learning is founded on novel algorithms and architectures for artificial neural networks together with the recent availability of very fast computers and massive datasets. It discovers multiple levels of distributed representations of the input, with higher levels representing more abstract concepts. We hypothesized that the construction of a hierarchy of chemical features gives Deep Learning the edge over other toxicity prediction methods. Furthermore, Deep Learning naturally enables multi-task learning, that is, learning of all toxic effects in one neural network and thereby learning of highly informative chemical features. In order to utilize Deep Learning for toxicity prediction, we have developed the DeepTox pipeline. First, DeepTox normalizes the chemical representations of the compounds. Then it computes a large number of chemical descriptors that are used as input to machine learning methods. In its next step, DeepTox trains models, evaluates them, and combines the best of them to ensembles. Finally, DeepTox predicts the toxicity of new compounds. In the Tox21 Data Challenge, DeepTox had the highest performance of all computational methods winning the grand challenge, the nuclear receptor panel, the stress response panel, and six single assays (teams “Bioinf@JKU”). We found that Deep Learning excelled in toxicity prediction and outperformed many other computational approaches like naive Bayes, support vector machines, and random forests.
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“Machine learning has also been implemented to assess the toxicity of molecules, for instance, using DeepTox, a DL-based model for evaluating the toxic effects of compounds based on a dataset containing many drug molecules [19].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [19]
In the citing paper
“When working on new test sets, CNN usually predicts with high accuracy [88].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [88]
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“Using CNN to automatically construct abstract chemical fragments, the deep learning model showed very high performance in toxicity prediction based on high-throughput data, with an average area under the curve (AUC) of 0.846 [88].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [88]
In the citing paper
“After extensive data training, CNN can identify those substructure or molecular fragments that might make a molecule toxic.”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [88]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
(2014) · Cited by 33,419
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
(2010) · International Conference on Machine Learning · Cited by 13,995
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Flagged first, then by citations
Research works printed but not linked: 2
- Svetnik, 2003, Random forest: a classification and regression tool for compound classification and QSAR modeling, J. Chem. Inf. Comput. Sci., 43, 1947, 10.1021/ci034160g · DOI
- Unterthiner, 2014, Deep learning as an opportunity in virtual screening
Cited by
Sorted by citations
Items 801–900 of 1,000
Sharma, R., Gulati, A., & Chopra, K. (2023) · Springer eBooks · doi:10.1007/978-981-99-6472-7_2 · Cited by 4
DTox: A deep neural network-based in visio lens for large scale toxicogenomics data
Hase, T., Ghosh, S., Aisaki, K., Kitajima, S., Kanno, J., Kitano, H., & Yachie‐Kinoshita, A. (2024) · The Journal of Toxicological Sciences · doi:10.2131/jts.49.105 · Cited by 3
The role of computational toxicology in the risk assessment of food products
Allen, T. E. H., Gutsell, S., & Punt, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-819470-6.00007-x · Cited by 3
Gupta, V. K., Gupta, A., Vishwakarma, S., & Negi, A. S. (2024) · IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) · doi:10.1109/iciteics61368.2024.10625237 · Cited by 3
Virtual Libraries for Docking Methods: Guidelines for the Selection and the Preparation
Sellami, A., Réau, M. F., Langenfeld, F., Lagarde, N., & Montès, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00017-5 · Cited by 3
Feature Selection on Imbalanced Data and Its Application on Toxicity Prediction
Li, J. (2020) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc51923.2020.9469564 · Cited by 3
Sun, X., Zhang, X., Wang, L., Li, Y., Muir, D. C. G., & Zeng, E. Y. (2021) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2021.126746 · Cited by 3
Advancements in a Zebrafish Model for Toxicity Assessment of Nanomaterials
Lee, S. L. J., & Lin, S. (2022) · Springer eBooks · doi:10.1007/978-981-16-9116-4_5 · Cited by 4
Artificial Intelligence in medicine
Sonawane, U., & Komorowski, M. (2023) · AI critique · doi:10.1515/9783839467664-009 · Cited by 3
Multi-task Deep Neural Networks in Automated Protein Function PredictionPreprint
Rifaioğlu, A. S., Doğan, T., Martin, M. J., Çetin-Atalay, R., & Atalay, V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.04802 · Cited by 3
Patient Informed Domain Adaptation Improves Clinical Drug Response PredictionPreprint
Anastopoulos, I. N., Seninge, L., Ding, H., & Stuart, J. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.07.455527 · Cited by 3
Machine Learning for Acute Oral System Toxicity Regression and ClassificationPreprint
Parks, C. D., Gaieb, Z., & Amaro, R. E. (2019) · ChemRxiv · doi:10.26434/chemrxiv.9733973.v1 · Cited by 3
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Limbu, S., Zakka, C., & Dakshanamurthy, S. (2021) · ChemRxiv · doi:10.26434/chemrxiv.13726258 · Cited by 3
Visualization, Exploration, and Screening of Chemical Space in Drug Discovery
Naveja, J. J., Saldívar‐González, F. I., Prado‐Romero, D. L., Ruiz-Moreno, A. J., Velasco-Velázquez, M. A., Miranda‐Quintana, R. A., & Medina‐Franco, J. L. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch16 · Cited by 4
Hruška, E., & Liu, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90049-2.00024-x · Cited by 3
FSL-CP: a benchmark for small molecule activity few-shot prediction using cell microscopy images
Ha, S. V., Leuschner, L., & Czodrowski, P. (2024) · Digital Discovery · doi:10.1039/d3dd00205e · Cited by 2
Yağın, F. H., Görmez, Y., Çolak, C., Algarni, A., Al-Hashem, F. H., & Ardigò, L. P. (2025) · Pharmaceuticals · doi:10.3390/ph18070975 · Cited by 2
Chou, W., Chen, W., & Chen, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30010-3.00002-7 · Cited by 2
Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development
Bobolea, S., Hinoveanu, M. I., Dimitriu, A., Brașoveanu, M., Iliescu, C., Dinu‐Pîrvu, C., Ghica, M. V., Anuța, V., Popa, L., & Prisada, R. M. (2025) · Drug Development Research · doi:10.1002/ddr.70182 · Cited by 2
Ouassaf, M., Zekri, A., Khan, S. U., Rengasamy, K. R. R., & Alhatlani, B. Y. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1646784 · Cited by 2
Machine Learning for Predicting Human Drug-Induced Cardiotoxicity: A Scoping Review
Han, J., Kim, M., Kim, H. W., Choi, K., Ju, S., & Kim, M. G. (2025) · Toxics · doi:10.3390/toxics13121087 · Cited by 2
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Mujtaba, M., Shafiq, M. I., Ahmad, A., Al-Otaibi, J. S., Daglia, M., & Khan, H. (2025) · Chemistry Africa · doi:10.1007/s42250-025-01452-4 · Cited by 2
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Ebner, A. T., Bartmann, C., Topf, S., Klambauer, G., Schimunek, J., & Luukkonen, S. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01148 · Cited by 3
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Kolobaric, A., Alagappan, S., Hoffman, L. C., Cozzolino, D., & Chapman, J. (2025) · Journal of Hazardous Materials Advances · doi:10.1016/j.hazadv.2025.100996 · Cited by 2
Cerimagic, T., Sosnin, S. D., & Ecker, G. F. (2026) · Digital Discovery · doi:10.1039/d5dd00536a · Cited by 2
Kolesnyk, S., Souza, J. D. P., Bearth, A., Aicher, L., Wilks, M., Mosig, A., & Fritsche, E. (2026) · Toxicology · doi:10.1016/j.tox.2026.154439 · Cited by 2
CheMLT-F: multitask learning in biochemistry through transformer fusion
Mun, V., & Fazli, S. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01199-1 · Cited by 2
Bridging diagnosis and therapeutics: The role of AI in cancer detection and drug development
Barua, S., Balaji, B., & Balaji, S. (2026) · Health Sciences Review · doi:10.1016/j.hsr.2026.100270 · Cited by 2
Kabdraisova, A. Z., Umbetova, A. K., Kairalapova, G., Litvinenko, Y. A., Sassykova, L. R., Yelibayeva, N. S., Burasheva, G. S., Berganayeva, A. E., Assylkhanov, Z. S., Dauletova, M. D., et al. (2026) · Molecules · doi:10.3390/molecules31122162 · Cited by 3
Application of In Silico Drug Repurposing in Infectious Diseases
Kumar, P., & Ghosh, I. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816125-8.00015-8 · Cited by 2
Drug Toxicity Prediction by Machine Learning Approaches
Shen, Y., Shih, F. Y., & Chen, H. (2023) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001423510138 · Cited by 2
Huang, S., & Hu, M. Y. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15339-6.00019-9 · Cited by 2
Arivanantham, S., Sekar, P., Sundhararajan, N., Nagalingam, Y., & Raju, S. K. (2023) · World Journal of Biology Pharmacy and Health Sciences · doi:10.30574/wjbphs.2023.16.2.0467 · Cited by 2
Rizwandy, R. R., Aditsania, A., & Kurniawan, I. (2023) · International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD) · doi:10.1109/icoabcd59879.2023.10390946 · Cited by 2
Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction
Santiago-Gonzalez, F., Martinez-Rodriguez, J. L., García-Pérez, C. A., Juárez-Saldívar, A., & Cruz, H. E. C. (2024) · Current Computer - Aided Drug Design · doi:10.2174/0115734099315538240909101737 · Cited by 2
Navigating the genomic landscape: A deep dive into clinical genetics with deep learning
Kaur, M. P., Singh, V., Khan, A., Sharma, K., Mendonça, F. J. B., & Nayarisseri, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27574-6.00006-0 · Cited by 2
From algorithm to applications: Artificial intelligence – A future prospective in medicine
Sriram, T., & Gladia Jenifer, B. (2025) · Sri Ramachandra Journal of Health Sciences · doi:10.25259/srjhs_16_2024 · Cited by 2
Capsule neural network and its applications in drug discovery
Wang, Y., Wang, B., Zou, J., Wu, A., Liu, Y., Wan, Y., Luo, J., & Wu, J. (2025) · iScience · doi:10.1016/j.isci.2025.112217 · Cited by 2
Bhagat, R. P., Amin, S. A., Sessa, L., Concilio, S., Piotto, S., & Gayen, S. (2025) · European Journal of Medicinal Chemistry Reports · doi:10.1016/j.ejmcr.2025.100295 · Cited by 2
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Sinha, M., Praveen, G. B., Sachan, D. K., & Parthasarathi, R. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_137 · Cited by 2
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Mahmoud, G. A., Riad, O. K. M., & Badawy, M. S. E. M. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.05.002 · Cited by 2
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Sampathi, S., & Bhatia, N. (2024) · Springer eBooks · doi:10.1007/978-981-97-2596-0_5 · Cited by 2
Lu, Y., Wang, C., Wang, Z., Zhang, X., Zhou, G., & Li, C. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.11.110 · Cited by 3
Understanding Compound Efflux from Gram-Negative Bacteria, a Final Frontier for Antibiotic Discovery
Ulrich, R. J., & Hergenrother, P. J. (2025) · Microbial Drug Resistance · doi:10.1089/mdr.2024.0195 · Cited by 2
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Sharma, A., & Khungar, B. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-16681-6_22 · Cited by 2
Skin sensitizer classification using dual-input machine learning model
Matsumura, K. (2020) · Chem-Bio Informatics Journal · doi:10.1273/cbij.20.54 · Cited by 2
Yadav, M. K., & Eswari, S. (2021) · Computers · doi:10.3390/computers10060074 · Cited by 2
Counterfactual based reinforcement learning for graph neural networks
Pham, D. H., & Zhang, Y. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04978-9 · Cited by 2
Predicting toxicity from chemical structure of a drug compound
Pawar, B., More, A., Nagaraja, S., Tekade, M., & Tekade, R. K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15840-7.00008-7 · Cited by 2
Kashyap, K., & Siddiqi, M. I. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-18638-7.00027-x · Cited by 2
Peng, Y., Zhang, H., Zhang, Z., Lin, Y., Zhou, S., & Qiao, S. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385367 · Cited by 2
Quantum Optical Experiments Modeled by Long Short-Term Memory
Adler, T., Erhard, M., Krenn, M., Brandstetter, J., Kofler, J., & Hochreiter, S. (2021) · Photonics · doi:10.3390/photonics8120535 · Cited by 3
Self-citationPersonalized Prognostic Models for Oncology: A Machine Learning ApproachPreprint
Dooling, D. J., Kim, A., McAneny, B. L., & Webster, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.07369 · Cited by 2
Computational Approaches for Target Inference
Heßler, G., Grebner, C., & Matter, H. (2019) · Methods and principles in medicinal chemistry · doi:10.1002/9783527818242.ch10 · Cited by 2
Multi Task Deep Learning for Genomic PredictionsPreprint
Guo, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.15.426878 · Cited by 2
Heterogeneous Graph based Deep Learning for Biomedical Network Link PredictionPreprint
Guo, J., Li, J., Leng, D., & Pan, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01649 · Cited by 2
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Guo, Z., Sharma, P. K., Du, L., & Abraham, R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.17.460864 · Cited by 2
Machine Learning Approaches in Computational Toxicology Studies
Ambure, P. S., Barigye, S. J., & Gozalbes, R. (2021) · Wiley eBooks · doi:10.1002/9781119681397.ch7 · Cited by 2
Choudhuri, S., Mallik, S., Ghosh, B., Si, T., Bhadra, T., Maulik, U., & Li, A. (2022) · Wiley eBooks · doi:10.1002/9781119743187.ch5 · Cited by 2
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Mani, G., & Jayakumar, G. (2024) · Wiley eBooks · doi:10.1002/9781394234196.ch4 · Cited by 2
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Liang, Y., Zhang, R., Li, Y., Huo, M., Ma, Z., Singh, D., Gao, C., Rahmani, H., Bandi, S., Zhang, L., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.29.615524 · Cited by 3
ViSAS for Entering Chemical Space: Virtual Screening of Analog Series and Related AdvancesPreprint
Naveja-Romero, J. J., Saldívar-González, F. I., Prado-Romero, D. L., Ruiz-Moreno, A. J., Velasco-Velázquez, M. A., Miranda-Quintana, R. A., & Medina‐Franco, J. L. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-9t2rj · Cited by 2
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Lakatos, I., Hajdú, A., & Harangi, B. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433953 · Cited by 1
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Lin, Z., Zhang, Z., Chen, C.-Y., Chen, Q., & Wu, P.-Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30010-3.00007-6 · Cited by 1
Zheng, M. (2025) · Springer eBooks · doi:10.1007/978-981-95-2525-6_48 · Cited by 1
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Mondal, P., Banerjee, P., & Majhi, V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13816-4.00003-6 · Cited by 1
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Ielanskyi, M., Wang, M., Scott, L., Rieber, L., Merrett, S. L., Schimunek, J., Mayr, A., McDowell, I. C., Klambauer, G., & Bowen, T. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag545 · Cited by 1
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Lin, J., Hostaš, J., Hu, A., Hu, H., Ooi, H. K., & Ghaemi, M. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-33443-3 · Cited by 1
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Aniceto, N., Freitas, A. A., & Ghafourian, T. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_18 · Cited by 2
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Simizo, A., de Morais, M., Vesco, M. F. [., & Nakaya, H. I. (2026) · Biochemical Society Transactions · doi:10.1042/bst20253023 · Cited by 1
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Metadata
- Authors
- Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S.
- Affiliations
- Johannes Kepler University of Linz (AT)
- Corresponding author
- Hochreiter, Sepp
- Venue
- Frontiers in Environmental Science
- Publisher
- Frontiers Media SA
- Published
- 2016-02-02
- Volume
- 3
- ISSN
- 2296-665X (ISSN-L)
- OpenAlex
- W2189911347
- Cited by
- 1,081
- References
- 89
- Citations by year
- 2026: 148 · 2025: 124 · 2024: 126 · 2023: 124 · 2022: 95 · 2021: 143 · 2020: 98 · 2019: 108 · 2018: 79 · 2017: 24 · 2016: 9
- FWCI
- 67.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Environmental Science (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- ChemBioBridge
- European Commission
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Keywords
- toxicity prediction, deep learning, multi-task learning, molecular descriptors, ensemble learning, environmental chemicals, drug toxicity
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