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DeepTox: Toxicity Prediction using Deep Learning
Open accessAbstract
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]
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“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]
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“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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Citations
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
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
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
Self-citationProTox-II: a webserver for the prediction of toxicity of chemicals
Banerjee, P., Eckert, A. P., Schrey, A. K., & Preißner, R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky318 · Cited by 3,312
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537
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
Shen, C. (2018) · Water Resources Research · doi:10.1029/2018wr022643 · Cited by 1,522
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,443
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. (2019) · Water Resources Research · doi:10.1029/2019wr026065 · Cited by 1,089
Self-citationArtificial intelligence in drug development: Present status and future prospects
Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018
K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
Jiménez-Luna, J., Škalič, M., Martínez-Rosell, G., & Fabritiis, G. D. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00650 · Cited by 946
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895
Machine Learning in Drug Discovery: A Review
Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10058-4 · Cited by 893
Adaptive Graph Convolutional Neural Networks
Li, R., Wang, S., Zhu, F., & Huang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11691 · Cited by 842
Layer-Wise Relevance Propagation: An Overview
Montavon, G., Binder, A., Lapuschkin, S., Samek, W., & Müller, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_10 · Cited by 883
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Low Data Drug Discovery with One-Shot Learning
Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.6b00367 · Cited by 799
Augmenting large language models with chemistry tools
M. Bran, A., Cox, S., Schilter, O., Baldassari, C., White, A. D., & Schwaller, P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00832-8 · Cited by 794
Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
Jaeger-Honz, S., Fulle, S., & Turk, S. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00616 · Cited by 759
A Structure-Based Drug Discovery Paradigm
Batool, M., Ahmad, S. B., & Choi, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112783 · Cited by 785
Deep Learning in Drug Discovery
Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723
Machine Learning in Computer-Aided Synthesis Planning
Coley, C. W., Green, W. H., & Jensen, K. F. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00087 · Cited by 704
Wallach, I., Dzamba, M., & Heifets, A. (2015) · arXiv · doi:10.48550/arxiv.1510.02855
Jiang, D., Wu, Z., Hsieh, C.-Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., & Hou, T. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-020-00479-8 · Cited by 664
Advancing drug discovery via artificial intelligence
Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.004 · Cited by 654
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Preuer, K., Lewis, R. P. I., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx806 · Cited by 668
Self-citationAn Introductory Review of Deep Learning for Prediction Models With Big Data
Emmert‐Streib, F., Yang, Z., Han, F., Tripathi, S., & Dehmer, M. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00004 · Cited by 651
Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565
Explainability Methods for Graph Convolutional Neural Networks
Pope, P. E., Kolouri, S., Rostami, M., Martin, C. E., & Hoffmann, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01103 · Cited by 553
Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction
Segler, M. H. S., & Waller, M. P. (2017) · Chemistry – A European Journal · doi:10.1002/chem.201605499 · Cited by 548
Karimi, M., Wu, D. J., Wang, Z., & Shen, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz111 · Cited by 540
Rifaioğlu, A. S., Ataş, H., Martin, M. J., Çetin-Atalay, R., Atalay, V., & Doğan, T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby061 · Cited by 549
Winter, R., Montanari, F., Noé, F., & Clevert, D.-A. (2018) · Chemical Science · doi:10.1039/c8sc04175j · Cited by 530
Convolutional embedding of attributed molecular graphs for physical property prediction
Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00601 · Cited by 470
Big data and artificial intelligence modeling for drug discovery
Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455
Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
Gentile, F., Agrawal, V., Hsing, M., Ton, A., Ban, F., Norinder, U., Gleave, M. E., & Cherkasov, A. R. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00229 · Cited by 478
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429
Large language models in healthcare and medical domain: A review
Nazi, Z. A., & Peng, W. (2024) · Informatics · doi:10.3390/informatics11030057 · Cited by 415
Graph convolutional networks for computational drug development and discovery
Sun, M., Zhao, S., Gilvary, C. M., Elemento, O., Zhou, J., & Wang, F. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz042 · Cited by 424
Boosting docking-based virtual screening with deep learning.
Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395
Applications of Deep Learning in Molecule Generation and Molecular Property Prediction
Walters, W. P., & Barzilay, R. (2020) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00699 · Cited by 412
Artificial Intelligence in Drug Design
Heßler, G., & Baringhaus, K. H. (2018) · Molecules · doi:10.3390/molecules23102520 · Cited by 396
ToxinPred2: an improved method for predicting toxicity of proteins
Sharma, N., Naorem, L. D., Jain, S., & Raghava, G. P. S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac174 · Cited by 380
Transfer Learning for Drug Discovery
Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L., & Pei, J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02147 · Cited by 376
Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis
Niazi, S. K., & Mariam, Z. (2023) · Pharmaceuticals · doi:10.3390/ph17010022 · Cited by 371
Deep learning in drug discovery: opportunities, challenges and future prospects
Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353
Jing, Y., Bian, Y., Hu, Z., Wang, L., & Xie, X.-Q. S. (2018) · The AAPS Journal · doi:10.1208/s12248-018-0210-0 · Cited by 360
ToxinPred 3.0: An improved method for predicting the toxicity of peptides
Rathore, A. S., Choudhury, S., Arora, A., Tijare, P. A., & Raghava, G. P. S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108926 · Cited by 365
Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017) · Oncotarget · doi:10.18632/oncotarget.14073 · Cited by 350
Korotcov, A. V., Tkachenko, V., Russo, D. P., & Ekins, S. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00578 · Cited by 367
Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development
Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N. M., Das, D., Bidarolli, M., & Sony, H. T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032026 · Cited by 347
Lenselink, E. B., ten Dijke, N., Bongers, B., Papadatos, G., van Vlijmen, H. W. T., Kowalczyk, W., IJzerman, A. P., & van Westen, G. J. P. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0232-0 · Cited by 334
Cang, Z., & Wei, G.-W. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005690 · Cited by 330
Self-Supervised Learning on Graphs: Contrastive, Generative, or Predictive
Wu, L., Lin, H., Tan, C., Gao, Z., & Li, S. Z. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3131584 · Cited by 345
Vișan, A. I., & Neguț, I. (2024) · Life · doi:10.3390/life14020233 · Cited by 333
AI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319
Koutsoukas, A., Monaghan, K. J., Li, X., & Huan, J. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0226-y · Cited by 315
“Two major crowd-sourced competitions, the Merck QSAR competition, held in 2012 at the Kaggle data mining platform, and the Tox21 data-challenge 2014 for chemical risk assessment were both won by groups that utilized deep learning methods as main machine learning techniques [19, 20].”
A review of computational drug repurposing
Park, K. (2019) · Translational and Clinical Pharmacology · doi:10.12793/tcp.2019.27.2.59 · Cited by 314
Bai, Q., Tan, S., Xu, T., Liu, H., Huang, J., & Yao, X. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa161 · Cited by 299
Artificial intelligence in drug design
Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z., Lu, D., Wu, X., Zhao, J., Tan, X., et al. (2018) · Science China Life Sciences · doi:10.1007/s11427-018-9342-2 · Cited by 293
Artificial intelligence for drug discovery: Resources, methods, and applications
Chen, W., Liu, X., Zhang, S., & Chen, S. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.02.019 · Cited by 301
Yang, H., Sun, L., Li, W., Liu, G., & Tang, Y. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00030 · Cited by 287
Blockchain and Artificial Intelligence Technology in E-Health
Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281
Zhang, J., Mucs, D., Norinder, U., & Svensson, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00633 · Cited by 289
Applying machine learning techniques to predict the properties of energetic materials
Elton, D. C., Boukouvalas, Z., Butrico, M. S., Fuge, M., & Chung, P. W. (2018) · Scientific Reports · doi:10.1038/s41598-018-27344-x · Cited by 279
Biological network analysis with deep learning
Muzio, G., O’Bray, L., & Borgwardt, K. M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa257 · Cited by 280
Cang, Z., Mu, L., & Wei, G.-W. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005929 · Cited by 257
The transformational role of GPU computing and deep learning in drug discovery
Pandey, M., Fernandez, M., Gentile, F., Isayev, O., Tropsha, A., Stern, A. C., & Cherkasov, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00463-x · Cited by 255
Xu, Y., Pei, J., & Lai, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00244 · Cited by 250
Integrating Machine Learning with Human Knowledge
Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020) · iScience · doi:10.1016/j.isci.2020.101656 · Cited by 243
Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response
Su, R., Liu, X., Wei, L., & Zou, Q. (2019) · Methods · doi:10.1016/j.ymeth.2019.02.009 · Cited by 242
DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks
Cao, H., Wang, J., He, L., Qi, Y., & Zhang, J. Z. H. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00697 · Cited by 244
Deep learning for drug response prediction in cancer
Baptista, D., Ferreira, P. G., & Rocha, M. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz171 · Cited by 243
The METLIN small molecule dataset for machine learning-based retention time prediction
Domingo-Almenara, X., Guijas, C., Billings, E., Montenegro-Burke, J. R., Uritboonthai, W., Aisporna, A., Chen, E., Benton, H. P., & Siuzdak, G. E. (2019) · Nature Communications · doi:10.1038/s41467-019-13680-7 · Cited by 244
The Next Era: Deep Learning in Pharmaceutical Research
Ekins, S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-016-2029-7 · Cited by 249
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
Zhao, L., Ciallella, H. L., Aleksunes, L. M., & Zhu, H. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.07.005 · Cited by 244
Artificial intelligence in drug discovery: applications and techniques
Deng, J., Yang, Z., Ojima, I., Samaras, D., & Wang, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab430 · Cited by 244
Artificial intelligence and machine learning in drug discovery and development
Patel, V. J., & Shah, M. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.10.001 · Cited by 234
A systematic study of key elements underlying molecular property prediction
Deng, J., Yang, Z., Wang, H., Ojima, I., Samaras, D., & Wang, F. (2023) · Nature Communications · doi:10.1038/s41467-023-41948-6 · Cited by 268
Goh, G. B., Siegel, C., Vishnu, A., Hodas, N. O., & Baker, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06689 · Cited by 251
Thakkar, A., Chadimová, V., Bjerrum, E. J., Engkvist, O., & Reymond, J. (2021) · Chemical Science · doi:10.1039/d0sc05401a · Cited by 246
Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery
Simm, J., Klambauer, G., Arany, Á., Steijaert, M. N., Wegner, J. K., Gustin, E. M., Chupakhin, V., Chong, Y. T., Vialard, J. E., Buijnsters, P. J. J. A., et al. (2018) · Cell chemical biology · doi:10.1016/j.chembiol.2018.01.015 · Cited by 228
Self-citationOcaña, A., Pandiella, A., Privat, C., Bravo, I., Luengo-Oroz, M. A., Amir, E., & Győrffy, B. (2025) · Biomarker Research · doi:10.1186/s40364-025-00758-2 · Cited by 248
Deep Learning in Protein Structural Modeling and Design
Gao, W., Mahajan, S. P., Sulam, J., & Gray, J. J. (2020) · Patterns · doi:10.1016/j.patter.2020.100142 · Cited by 227
Ciallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00393 · Cited by 227
Wenzel, J., Matter, H., & Schmidt, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00785 · Cited by 230
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Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point
Cavasotto, C. N., & Scardino, V. (2022) · ACS Omega · doi:10.1021/acsomega.2c05693 · Cited by 234
Machine learning for synergistic network pharmacology: a comprehensive overview
Noor, F., Asif, M., Ashfaq, U. A., Qasim, M., & Qamar, M. T. U. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad120 · Cited by 226
Hirohara, M., Saitō, Y., Koda, Y., Sato, K., & Sakakibara, Y. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2523-5 · Cited by 217
Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades
Göller, A. H., Kuhnke, L., Montanari, F., Bonin, A., Schneckener, S., ter Laak, A., Wichard, J., Lobell, M., & Hillisch, A. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.07.001 · Cited by 215
eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212
“DeepTox [30] was the best performing methods in the Tox21 Data Challenge winning the grand challenge, the nuclear receptor panel, the stress response panel, and six single assays.”
MethodResultDeep learning in pharmacogenomics: from gene regulation to patient stratification
Kalinin, A. A., Higgins, G. A., Reamaroon, N., Soroushmehr, S., Allyn-Feuer, A., Dinov, I. D., Najarian, K., & Athey, B. D. (2018) · Pharmacogenomics · doi:10.2217/pgs-2018-0008 · Cited by 207
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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