{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:37:41Z","timestamp":1784644661536,"version":"3.55.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004733","name":"universidade de macau","doi-asserted-by":"publisher","award":["MYRG2019-00098-FST"],"award-info":[{"award-number":["MYRG2019-00098-FST"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases.\u00a0Our optimal model\u00a0SSL-GCN is hosted on an online server accessible\u00a0through:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/app.cbbio.online\/ssl-gcn\/home\">https:\/\/app.cbbio.online\/ssl-gcn\/home<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1186\/s13321-021-00570-8","type":"journal-article","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T04:02:41Z","timestamp":1637985761000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network"],"prefix":"10.1186","volume":"13","author":[{"given":"Jiarui","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yain-Whar","family":"Si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chon-Wai","family":"Un","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3695-7758","authenticated-orcid":false,"given":"Shirley W. I.","family":"Siu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"issue":"26","key":"570_CR1","doi-asserted-by":"publisher","first-page":"12660","DOI":"10.1073\/pnas.1816039116","volume":"116","author":"EJ Llanos","year":"2019","unstructured":"Llanos EJ, Leal W, Luu DH, Jost J, Stadler PF, Restrepo G (2019) Exploration of the chemical space and its three historical regimes. Proc Natl Acad Sci 116(26):12660\u201312665","journal-title":"Proc Natl Acad Sci"},{"issue":"5","key":"570_CR2","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.cbpa.2007.08.033","volume":"11","author":"C McInnes","year":"2007","unstructured":"McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11(5):494\u2013502","journal-title":"Curr Opin Chem Biol"},{"key":"570_CR3","volume-title":"Virtual screening for bioactive molecules","author":"H Kubinyi","year":"2008","unstructured":"Kubinyi H, Mannhold R, Timmerman H (2008) Virtual screening for bioactive molecules, vol 10. Wiley, Weinheim"},{"key":"570_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-28014-6","volume-title":"Screening: methods for experimentation in industry, drug discovery, and genetics","author":"A Dean","year":"2006","unstructured":"Dean A, Lewis S (2006) Screening: methods for experimentation in industry, drug discovery, and genetics. Springer, Berlin"},{"issue":"4","key":"570_CR5","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.cbpa.2004.06.008","volume":"8","author":"TI Oprea","year":"2004","unstructured":"Oprea TI, Matter H (2004) Integrating virtual screening in lead discovery. Curr Opin Chem Biol 8(4):349\u2013358","journal-title":"Curr Opin Chem Biol"},{"key":"570_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s12910-019-0352-3","volume":"20","author":"J Bailey","year":"2019","unstructured":"Bailey J, Balls M (2019) Recent efforts to elucidate the scientific validity of animal-based drug tests by the pharmaceutical industry, pro-testing lobby groups, and animal welfare organisations. BMC Med Ethics 20:16","journal-title":"BMC Med Ethics"},{"issue":"1","key":"570_CR7","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s40360-018-0282-6","volume":"20","author":"L Pu","year":"2019","unstructured":"Pu L, Naderi M, Liu T, Wu H-C, Mukhopadhyay S, Brylinski M (2019) e toxpred: a machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol 20(1):2","journal-title":"BMC Pharmacol Toxicol"},{"issue":"2","key":"570_CR8","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1002\/wcms.1240","volume":"6","author":"AB Raies","year":"2016","unstructured":"Raies AB, Bajic VB (2016) In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscipl Rev Comput Mol Sci 6(2):147\u2013172","journal-title":"Wiley Interdiscipl Rev Comput Mol Sci"},{"issue":"1","key":"570_CR9","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1093\/toxsci\/56.1.8","volume":"56","author":"JD McKinney","year":"2000","unstructured":"McKinney JD, Richard A, Waller C, Newman MC, Gerberick F (2000) The practice of structure activity relationships (SAR) in toxicology. Toxicol Sci 56(1):8\u201317","journal-title":"Toxicol Sci"},{"key":"570_CR10","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/B978-0-12-801505-6.00007-7","volume-title":"Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment","author":"K Roy","year":"2015","unstructured":"Roy K, Kar S, Das R (2015) Chapter 7\u2014validation of qsar models. In: Roy K, Kar S, Das RN (eds) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic press, Cambridge, pp 231\u2013289"},{"issue":"8","key":"570_CR11","doi-asserted-by":"publisher","first-page":"2358","DOI":"10.3390\/ijms19082358","volume":"19","author":"Y Wu","year":"2018","unstructured":"Wu Y, Wang G (2018) Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis. Int J Mol Sci 19(8):2358","journal-title":"Int J Mol Sci"},{"issue":"4","key":"570_CR12","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/10590501.2018.1537118","volume":"36","author":"G Idakwo","year":"2018","unstructured":"Idakwo G, Luttrell J, Chen M, Hong H, Zhou Z, Gong P, Zhang C (2018) A review on machine learning methods for in silico toxicity prediction. J Environ Sci Health Part C 36(4):169\u2013191","journal-title":"J Environ Sci Health Part C"},{"key":"570_CR13","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3389\/fchem.2018.00030","volume":"6","author":"H Yang","year":"2018","unstructured":"Yang H, Sun L, Li W, Liu G, Tang Y (2018) In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Front Chem 6:30. https:\/\/doi.org\/10.3389\/fchem.2018.00030","journal-title":"Front Chem"},{"issue":"8","key":"570_CR14","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","volume":"30","author":"S Kearnes","year":"2016","unstructured":"Kearnes S, McCloskey K, Berndl M, Pande V, Riley P (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Design 30(8):595\u2013608","journal-title":"J Comput Aided Mol Design"},{"key":"570_CR15","unstructured":"Li J, Cai D, He X (2017) Learning graph-level representation for drug discovery. arXiv preprint arXiv:1709.03741"},{"issue":"14","key":"570_CR16","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1016\/j.scib.2020.04.006","volume":"65","author":"F Wang","year":"2020","unstructured":"Wang F, Yang JF, Wang MY, Jia CY, Shi XX, Hao GF, Yang GF (2020) Graph attention convolutional neural network model for chemical poisoning of honey bees\u2019 prediction. Sci Bull 65(14):1184\u20131191","journal-title":"Sci Bull"},{"issue":"7","key":"570_CR17","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1021\/ci400187y","volume":"53","author":"A Lusci","year":"2013","unstructured":"Lusci A, Pollastri G, Baldi P (2013) Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inform Model 53(7):1563\u20131575","journal-title":"J Chem Inform Model"},{"issue":"11","key":"570_CR18","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1021\/acscentsci.8b00507","volume":"4","author":"EN Feinberg","year":"2018","unstructured":"Feinberg EN, Sur D, Wu Z, Husic BE, Mai H, Li Y, Sun S, Yang J, Ramsundar B, Pande VS (2018) Potentialnet for molecular property prediction. ACS Central Sci 4(11):1520\u20131530","journal-title":"ACS Central Sci"},{"key":"570_CR19","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.eswa.2017.12.020","volume":"97","author":"I Portugal","year":"2018","unstructured":"Portugal I, Alencar P, Cowan D (2018) The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst Appl 97:205\u2013227","journal-title":"Expert Syst Appl"},{"issue":"4","key":"570_CR20","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","volume":"3","author":"H Altae-Tran","year":"2017","unstructured":"Altae-Tran H, Ramsundar B, Pappu AS, Pande V (2017) Low data drug discovery with one-shot learning. ACS Central Sci 3(4):283\u2013293","journal-title":"ACS Central Sci"},{"key":"570_CR21","doi-asserted-by":"publisher","first-page":"176005","DOI":"10.1109\/ACCESS.2020.3023800","volume":"8","author":"B Rao","year":"2020","unstructured":"Rao B, Zhang L, Zhang G (2020) Acp-gcn: the identification of anticancer peptides based on graph convolution networks. IEEE Access 8:176005\u2013176011","journal-title":"IEEE Access"},{"key":"570_CR22","doi-asserted-by":"crossref","unstructured":"Li G, Muller M, Thabet A, Ghanem B (2019) Deepgcns: can gcns go as deep as cnns? In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9267\u20139276","DOI":"10.1109\/ICCV.2019.00936"},{"key":"570_CR23","doi-asserted-by":"crossref","unstructured":"Tang L, Liu H (2009) Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 817\u2013826","DOI":"10.1145\/1557019.1557109"},{"key":"570_CR24","doi-asserted-by":"crossref","unstructured":"Marcheggiani D, Titov I (2017) Encoding sentences with graph convolutional networks for semantic role labeling. arXiv preprint arXiv:1703.04826","DOI":"10.18653\/v1\/D17-1159"},{"key":"570_CR25","doi-asserted-by":"crossref","unstructured":"Bastings J, Titov I, Aziz W, Marcheggiani D, Sima\u2019an K (2017) Graph convolutional encoders for syntax-aware neural machine translation. arXiv preprint arXiv:1704.04675","DOI":"10.18653\/v1\/D17-1209"},{"key":"570_CR26","doi-asserted-by":"crossref","unstructured":"Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"key":"570_CR27","unstructured":"Monti F, Bronstein MM, Bresson X (2017) Geometric matrix completion with recurrent multi-graph neural networks. arXiv preprint arXiv:1704.06803"},{"key":"570_CR28","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"issue":"4","key":"570_CR29","doi-asserted-by":"publisher","first-page":"626","DOI":"10.3390\/biom10040626","volume":"10","author":"J Chen","year":"2020","unstructured":"Chen J, Siu SW (2020) Machine learning approaches for quality assessment of protein structures. Biomolecules 10(4):626","journal-title":"Biomolecules"},{"key":"570_CR30","first-page":"3","volume":"160","author":"SB Kotsiantis","year":"2007","unstructured":"Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Emerg Artif Intell Appl Comput Eng 160:3\u201324","journal-title":"Emerg Artif Intell Appl Comput Eng"},{"key":"570_CR31","doi-asserted-by":"crossref","unstructured":"Cui W, Liu Y, Li Y, Guo M, Li Y, Li X, Wang T, Zeng X, Ye, C (2019) Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: International conference on information processing in medical imaging. Springer, pp 554\u2013565","DOI":"10.1007\/978-3-030-20351-1_43"},{"issue":"2","key":"570_CR32","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE Van Engelen","year":"2020","unstructured":"Van Engelen JE, Hoos HH (2020) A survey on semi-supervised learning. Mach Learn 109(2):373\u2013440","journal-title":"Mach Learn"},{"key":"570_CR33","unstructured":"Rasmus A, Valpola H, Honkala M, Berglund M, Raiko T (2015) Semi-supervised learning with ladder networks. arXiv preprint arXiv:1507.02672"},{"key":"570_CR34","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. arXiv preprint arXiv:1703.01780"},{"key":"570_CR35","unstructured":"Laine S, Aila T (2016) Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242"},{"key":"570_CR36","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"2","key":"570_CR37","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswing K, Pande V (2018) Moleculenet: a benchmark for molecular machine learning. Chem Sci 9(2):513\u2013530","journal-title":"Chem Sci"},{"issue":"4","key":"570_CR38","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1021\/ci400084k","volume":"53","author":"RP Sheridan","year":"2013","unstructured":"Sheridan RP (2013) Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inform Model 53(4):783\u2013790","journal-title":"J Chem Inform Model"},{"issue":"15","key":"570_CR39","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1021\/jm9602928","volume":"39","author":"GW Bemis","year":"1996","unstructured":"Bemis GW, Murcko MA (1996) The properties of known drugs. 1. molecular frameworks. J Med Chem 39(15):2887\u20132893","journal-title":"J Med Chem"},{"key":"570_CR40","unstructured":"RDKit: Open-Source Cheminformatics Software (2006). https:\/\/www.rdkit.org\/ Accessed 14 July 2021"},{"key":"570_CR41","unstructured":"Wang M, Yu L, Zheng D, Gan Q, Gai Y, Ye Z, Li M, Zhou J, Huang Q, Ma C et al. (2019) Deep graph library: towards efficient and scalable deep learning on graphs"},{"key":"570_CR42","unstructured":"Bergstra J, Yamins D, Cox D (2013) Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In: International conference on machine learning, pp 115\u2013123. PMLR"},{"key":"570_CR43","unstructured":"DGL: Deep Graph Library (2018). https:\/\/github.com\/dmlc\/dgl. Accessed 14 July 2021"},{"key":"570_CR44","unstructured":"DGL-LifeSci (2020). https:\/\/github.com\/awslabs\/dgl-lifesci. Accessed 14 July 2021"},{"key":"570_CR45","unstructured":"Hyperopt: Distributed Hyperparameter Optimization (2018). https:\/\/github.com\/hyperopt\/hyperopt. Accessed 14 July 2021"},{"key":"570_CR46","unstructured":"Ramsundar B, Eastman P, Walters P, Pande V, Leswing K, Wu Z (2019) Deep learning for the life sciences. O\u2019Reilly Media, 1005 Gravenstein Highway North, Sebastopol, CA 95472, USA"},{"key":"570_CR47","unstructured":"DeepChem (2015). https:\/\/github.com\/deepchem\/deepchem. Accessed 14 July 2021"},{"key":"570_CR48","unstructured":"Mean teachers are better role models (2018). https:\/\/github.com\/CuriousAI\/mean-teacher. Accessed 17 Oct 2021"},{"issue":"5","key":"570_CR49","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inform Model 50(5):742\u2013754","journal-title":"J Chem Inform Model"},{"issue":"1","key":"570_CR50","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1002\/qsar.200390007","volume":"22","author":"A Tropsha","year":"2003","unstructured":"Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of qspr models. QSAR Combinatorial Sci 22(1):69\u201377","journal-title":"QSAR Combinatorial Sci"},{"issue":"13","key":"570_CR51","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.1021\/jm010488u","volume":"45","author":"M Shen","year":"2002","unstructured":"Shen M, LeTiran A, Xiao Y, Golbraikh A, Kohn H, Tropsha A (2002) Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing pls methods. J Med Chem 45(13):2811\u20132823","journal-title":"J Med Chem"},{"key":"570_CR52","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learning Res 12:2825\u20132830","journal-title":"J Mach Learning Res"},{"issue":"8","key":"570_CR53","doi-asserted-by":"publisher","first-page":"2068","DOI":"10.1021\/acs.jcim.7b00146","volume":"57","author":"B Ramsundar","year":"2017","unstructured":"Ramsundar B, Liu B, Wu Z, Verras A, Tudor M, Sheridan RP, Pande V (2017) Is multitask deep learning practical for pharma? J Chem Inform Model 57(8):2068\u20132076","journal-title":"J Chem Inform Model"},{"key":"570_CR54","unstructured":"Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, G\u00f3mez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292"},{"issue":"4","key":"570_CR55","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1021\/ci8004379","volume":"49","author":"SJ Swamidass","year":"2009","unstructured":"Swamidass SJ, Azencott C-A, Lin T-W, Gramajo H, Tsai S-C, Baldi P (2009) Influence relevance voting: an accurate and interpretable virtual high throughput screening method. J Chem Inform Model 49(4):756\u2013766","journal-title":"J Chem Inform Model"},{"key":"570_CR56","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd international conference on knowledge discovery and data mining, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"570_CR57","volume-title":"On outliers and activity cliffs why QSAR often disappoints","author":"GM Maggiora","year":"2006","unstructured":"Maggiora GM (2006) On outliers and activity cliffs why QSAR often disappoints. ACS Publications, Washington, D.C."},{"issue":"6","key":"570_CR58","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1007\/s12257-020-0049-y","volume":"25","author":"H Kim","year":"2020","unstructured":"Kim H, Kim E, Lee I, Bae B, Park M, Nam H (2020) Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches. Biotechnol Bioprocess Eng 25(6):895\u2013930","journal-title":"Biotechnol Bioprocess Eng"},{"issue":"1","key":"570_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms15932","volume":"8","author":"P Kohonen","year":"2017","unstructured":"Kohonen P, Parkkinen JA, Willighagen EL, Ceder R, Wennerberg K, Kaski S, Grafstr\u00f6m RC (2017) A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury. Nat Commun 8(1):1\u201315","journal-title":"Nat Commun"},{"issue":"4","key":"570_CR60","doi-asserted-by":"publisher","first-page":"0176284","DOI":"10.1371\/journal.pone.0176284","volume":"12","author":"HA Rueda-Z\u00e1rate","year":"2017","unstructured":"Rueda-Z\u00e1rate HA, Imaz-Rosshandler I, C\u00e1rdenas-Ovando RA, Castillo-Fern\u00e1ndez JE, Noguez-Monroy J, Rangel-Escare\u00f1o C (2017) A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database. PLoS ONE 12(4):0176284","journal-title":"PLoS ONE"},{"issue":"4","key":"570_CR61","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1109\/TCBB.2018.2858756","volume":"16","author":"R Su","year":"2018","unstructured":"Su R, Wu H, Xu B, Liu X, Wei L (2018) Developing a multi-dose computational model for drug-induced hepatotoxicity prediction based on toxicogenomics data. IEEE\/ACM Trans Comput Biol Bioinformatics 16(4):1231\u20131239","journal-title":"IEEE\/ACM Trans Comput Biol Bioinformatics"},{"issue":"1","key":"570_CR62","doi-asserted-by":"publisher","first-page":"2000196","DOI":"10.1002\/minf.202000196","volume":"40","author":"T Blaschke","year":"2021","unstructured":"Blaschke T, Feldmann C, Bajorath J (2021) Prediction of promiscuity cliffs using machine learning. Mol Inform 40(1):2000196","journal-title":"Mol Inform"},{"key":"570_CR63","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412"},{"key":"570_CR64","doi-asserted-by":"crossref","unstructured":"Verma V, Kawaguchi K, Lamb A, Kannala J, Bengio Y, Lopez-Paz D (2019) Interpolation consistency training for semi-supervised learning. arXiv preprint arXiv:1903.03825","DOI":"10.24963\/ijcai.2019\/504"},{"key":"570_CR65","unstructured":"Berthelot D, Carlini N, Cubuk ED, Kurakin A, Sohn K, Zhang H, Raffel C (2019) Remixmatch: semi-supervised learning with distribution alignment and augmentation anchoring. arXiv preprint arXiv:1911.09785"},{"key":"570_CR66","unstructured":"Sohn K, Berthelot D, Li C-L, Zhang Z, Carlini N, Cubuk ED, Kurakin A, Zhang H, Raffel C (2020) Fixmatch: simplifying semi-supervised learning with consistency and confidence. arXiv preprint arXiv:2001.07685"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00570-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-021-00570-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00570-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T16:26:47Z","timestamp":1638030407000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-021-00570-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,27]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["570"],"URL":"https:\/\/doi.org\/10.1186\/s13321-021-00570-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-733550\/v1","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,27]]},"assertion":[{"value":"20 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"93"}}