{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T13:24:43Z","timestamp":1780752283288,"version":"3.54.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2020,5,13]],"date-time":"2020-05-13T00:00:00Z","timestamp":1589328000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010676","name":"H2020 Societal Challenges","doi-asserted-by":"publisher","award":["826121"],"award-info":[{"award-number":["826121"]}],"id":[{"id":"10.13039\/100010676","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of complementary sources of information might strengthen the accuracy of a drug efficacy prediction model. Here, we present a web-based platform for the Prediction of AntiCancer Compound sensitivity with Multimodal Attention-based Neural Networks (PaccMann). PaccMann is trained on public transcriptomic cell line profiles, compound structure information and drug sensitivity screenings, and outperforms state-of-the-art methods on anticancer drug sensitivity prediction. On the open-access web service (https:\/\/ibm.biz\/paccmann-aas), users can select a known drug compound or design their own compound structure in an interactive editor, perform in-silico drug testing and investigate compound efficacy on publicly available or user-provided transcriptomic profiles. PaccMann leverages methods for model interpretability and outputs confidence scores as well as attention heatmaps that highlight the genes and chemical sub-structures that were more important to make a prediction, hence facilitating the understanding of the model\u2019s decision making and the involved biochemical processes. We hope to serve the community with a toolbox for fast and efficient validation in drug repositioning or lead compound identification regimes.<\/jats:p>","DOI":"10.1093\/nar\/gkaa327","type":"journal-article","created":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T19:09:43Z","timestamp":1587582583000},"page":"W502-W508","source":"Crossref","is-referenced-by-count":66,"title":["PaccMann: a web service for interpretable anticancer compound sensitivity prediction"],"prefix":"10.1093","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4410-2805","authenticated-orcid":false,"given":"Joris","family":"Cadow","sequence":"first","affiliation":[{"name":"Computational Systems Biology Group, IBM Research Europe, S\u00e4umerstrasse 4, R\u00fcschlikon, 8803, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8307-5670","authenticated-orcid":false,"given":"Jannis","family":"Born","sequence":"first","affiliation":[{"name":"Computational Systems Biology Group, IBM Research Europe, S\u00e4umerstrasse 4, R\u00fcschlikon, 8803, Switzerland"},{"name":"Machine Learning\u00a0& Computational Biology Lab, D-BSSE, ETH Z\u00fcrich,\u00a0Mattenstrasse 26, Basel, 4058, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8872-0269","authenticated-orcid":false,"given":"Matteo","family":"Manica","sequence":"first","affiliation":[{"name":"Computational Systems Biology Group, IBM Research Europe, S\u00e4umerstrasse 4, R\u00fcschlikon, 8803, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Oskooei","sequence":"first","affiliation":[{"name":"Computational Systems Biology Group, IBM Research Europe, S\u00e4umerstrasse 4, R\u00fcschlikon, 8803, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3766-4233","authenticated-orcid":false,"given":"Mar\u00eda","family":"Rodr\u00edguez\u00a0Mart\u00ednez","sequence":"first","affiliation":[{"name":"Computational Systems Biology Group, IBM Research Europe, S\u00e4umerstrasse 4, R\u00fcschlikon, 8803, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,5,13]]},"reference":[{"key":"2020062614050317700_B1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1093\/biostatistics\/kxx069","article-title":"Estimation of clinical trial success rates and related parameters","volume":"20","author":"Wong","year":"2019","journal-title":"Biostatistics"},{"key":"2020062614050317700_B2","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1038\/nrd2898","article-title":"Assessing the translatability of drug projects: what needs to be scored to predict success","volume":"8","author":"Wehling","year":"2009","journal-title":"Nat. Rev. Drug. Discov."},{"key":"2020062614050317700_B3","doi-asserted-by":"crossref","first-page":"eaaw8412","DOI":"10.1126\/scitranslmed.aaw8412","article-title":"Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials","volume":"11","author":"Lin","year":"2019","journal-title":"Sci. Transl. Med."},{"key":"2020062614050317700_B4","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1186\/s13059-016-1050-9","article-title":"Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models","volume":"17","author":"Geeleher","year":"2016","journal-title":"Genome Biol."},{"key":"2020062614050317700_B5","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The rise of deep learning in drug discovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug Discov. Today"},{"key":"2020062614050317700_B6","doi-asserted-by":"crossref","first-page":"e61318","DOI":"10.1371\/journal.pone.0061318","article-title":"Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties","volume":"8","author":"Menden","year":"2013","journal-title":"PLoS One"},{"key":"2020062614050317700_B7","doi-asserted-by":"crossref","first-page":"8857","DOI":"10.1038\/s41598-018-27214-6","article-title":"Cancer drug response profile scan (CDRscan): a deep learning model that predicts drug effectiveness from cancer genomic signature","volume":"8","author":"Chang","year":"2018","journal-title":"Sci. Rep."},{"key":"2020062614050317700_B8","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ymeth.2014.08.005","article-title":"Molecular fingerprint similarity search in virtual screening","volume":"71","author":"Cereto-Massagu\u00e9","year":"2015","journal-title":"Methods"},{"key":"2020062614050317700_B9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. Chem. Inf. Comp. Sci."},{"key":"2020062614050317700_B10","article-title":"SMILES enumeration as data augmentation for neural network modeling of molecules","author":"Bjerrum","year":"2017"},{"key":"2020062614050317700_B11","doi-asserted-by":"crossref","first-page":"4757","DOI":"10.1021\/acs.molpharmaceut.9b00520","article-title":"Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders","volume":"16","author":"Manica","year":"2019","journal-title":"Mol. Pharm."},{"key":"2020062614050317700_B12","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.tips.2019.06.001","article-title":"The missing pieces of artificial intelligence in medicine","volume":"40","author":"Gilvary","year":"2019","journal-title":"Trends Pharmacol. Sci."},{"key":"2020062614050317700_B13","first-page":"5574","article-title":"What uncertainties do we need in bayesian deep learning for computer vision?","volume-title":"Advances in Neural Information Processing Systems 30","author":"Kendall","year":"2017"},{"key":"2020062614050317700_B14","doi-asserted-by":"crossref","first-page":"D955","DOI":"10.1093\/nar\/gks1111","article-title":"Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells","volume":"41","author":"Yang","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"2020062614050317700_B15","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2020062614050317700_B16","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1038\/nbt.2877","article-title":"A community effort to assess and improve drug sensitivity prediction algorithms","volume":"32","author":"Costello","year":"2014","journal-title":"Nat. Biotechnol."},{"key":"2020062614050317700_B17","doi-asserted-by":"crossref","first-page":"15918","DOI":"10.1038\/s41598-019-52093-w","article-title":"Network-based biased tree ensembles (NetBiTE) for drug sensitivity prediction and drug sensitivity biomarker identification in cancer","volume":"9","author":"Oskooei","year":"2019","journal-title":"Sci. Rep."},{"key":"2020062614050317700_B18","doi-asserted-by":"crossref","first-page":"D447","DOI":"10.1093\/nar\/gku1003","article-title":"STRING v10: protein\u2013protein interaction networks, integrated over the tree of life","volume":"43","author":"Szklarczyk","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"2020062614050317700_B19","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","article-title":"Adjusting batch effects in microarray expression data using empirical Bayes methods","volume":"8","author":"Johnson","year":"2007","journal-title":"Biostatistics"},{"key":"2020062614050317700_B20","first-page":"1050","article-title":"Dropout as a bayesian approximation: representing model uncertainty in deep learning","volume-title":"Proceedings of The 33rd International Conference on Machine Learning","author":"Gal","year":"2016"},{"key":"2020062614050317700_B21","first-page":"1","article-title":"Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks","volume-title":"International conference on Medical Imaging with Deep Learning","author":"Ayhan","year":"2018"},{"key":"2020062614050317700_B22","doi-asserted-by":"crossref","DOI":"10.1101\/868067","article-title":"Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) Model for Protein Kinase Inhibitor Response Prediction","author":"Huang","year":"2019"},{"key":"2020062614050317700_B23","article-title":"PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks","author":"Oskooei","year":"2018"},{"key":"2020062614050317700_B24","volume-title":"Bokeh: Python library for interactive visualization","author":"Bokeh Development Team","year":"2019"},{"key":"2020062614050317700_B25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.canlet.2012.01.005","article-title":"mTOR inhibitors in cancer therapy","volume":"319","author":"Zaytseva","year":"2012","journal-title":"Cancer Lett."},{"key":"2020062614050317700_B26","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1111\/j.1349-7006.2011.01967.x","article-title":"Inhibition of mTOR by temsirolimus contributes to prolonged survival of mice with pleural dissemination of non-small-cell lung cancer cells","volume":"102","author":"Ohara","year":"2011","journal-title":"Cancer Sci."},{"key":"2020062614050317700_B27","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1634\/theoncologist.2007-0171","article-title":"The potential role of mTOR inhibitors in non-small cell lung cancer","volume":"13","author":"Gridelli","year":"2008","journal-title":"Oncologist"},{"key":"2020062614050317700_B28","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.amjms.2016.08.014","article-title":"Targeting the mammalian target of rapamycin in lung cancer","volume":"352","author":"Vicary","year":"2016","journal-title":"Am. J. Med. Sci."},{"key":"2020062614050317700_B29","doi-asserted-by":"crossref","first-page":"778","DOI":"10.3389\/fphar.2018.00778","article-title":"Therapeutic effect of repurposed temsirolimus in lung adenocarcinoma model","volume":"9","author":"Chang","year":"2018","journal-title":"Front. Pharmacol."},{"key":"2020062614050317700_B30","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s12253-016-0066-5","article-title":"The gene expression status of the PI3K\/AKT\/mTOR pathway in gastric cancer tissues and cell lines","volume":"22","author":"Riquelme","year":"2016","journal-title":"Pathol. Oncol. Res."},{"key":"2020062614050317700_B31","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1186\/s13046-019-1121-3","article-title":"A subset of diffuse-type gastric cancer is susceptible to mTOR inhibitors and checkpoint inhibitors","volume":"38","author":"Fukamachi","year":"2019","journal-title":"J. Exp. Clin. Canc. Res."},{"key":"2020062614050317700_B32","doi-asserted-by":"crossref","first-page":"5565","DOI":"10.1200\/jco.2014.32.15_suppl.5565","article-title":"Temsirolimus in women with platinum-resistant ovarian cancer or advanced\/recurrent endometrial cancer: a multicenter phase II trial of the AGO Study Group (AGO-GYN 8)","volume":"32","author":"Emons","year":"2014","journal-title":"J .Clin. Oncol."},{"key":"2020062614050317700_B33","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.ygyno.2015.12.025","article-title":"Temsirolimus in women with platinum-refractory\/resistant ovarian cancer or advanced\/recurrent endometrial carcinoma. A phase II study of the AGO-study group (AGO-GYN8)","volume":"140","author":"Emons","year":"2016","journal-title":"Gynecol. Oncol."},{"key":"2020062614050317700_B34","doi-asserted-by":"crossref","first-page":"W90","DOI":"10.1093\/nar\/gkw377","article-title":"Enrichr: a comprehensive gene set enrichment analysis web server 2016 update","volume":"44","author":"Kuleshov","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"2020062614050317700_B35","doi-asserted-by":"crossref","first-page":"D183","DOI":"10.1093\/nar\/gkw1138","article-title":"PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements","volume":"45","author":"Mi","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"2020062614050317700_B36","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/nature21036","article-title":"Translation from unconventional 5 start sites drives tumour initiation","volume":"541","author":"Sendoel","year":"2017","journal-title":"Nature"},{"key":"2020062614050317700_B37","doi-asserted-by":"crossref","first-page":"W43","DOI":"10.1093\/nar\/gkz337","article-title":"DrugComb: an integrative cancer drug combination data portal","volume":"47","author":"Zagidullin","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"2020062614050317700_B38","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","article-title":"DeepSynergy: predicting anti-cancer drug synergy with Deep Learning","volume":"34","author":"Preuer","year":"2018","journal-title":"Bioinformatics"},{"key":"2020062614050317700_B39","doi-asserted-by":"crossref","first-page":"2413","DOI":"10.1093\/bioinformatics\/btx162","article-title":"SynergyFinder: a web application for analyzing drug combination dose\u2013response matrix data","volume":"33","author":"Ianevski","year":"2017","journal-title":"Bioinformatics"},{"key":"2020062614050317700_B40","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/1745-6150-8-28","article-title":"DrugMint: a webserver for predicting and designing of drug-like molecules","volume":"8","author":"Dhanda","year":"2013","journal-title":"Biol. Direct."},{"key":"2020062614050317700_B41","doi-asserted-by":"crossref","first-page":"W350","DOI":"10.1093\/nar\/gkz391","article-title":"Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning","volume":"47","author":"Ab\u00a0Ghani","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"2020062614050317700_B42","doi-asserted-by":"crossref","first-page":"e0191838","DOI":"10.1371\/journal.pone.0191838","article-title":"CLC-Pred: a freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds","volume":"13","author":"Lagunin","year":"2018","journal-title":"PLoS One"},{"key":"2020062614050317700_B43","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.ddtec.2004.10.009","article-title":"Scaffold hopping","volume":"1","author":"B\u00f6hm","year":"2004","journal-title":"Drug Discov. Today Technol."},{"key":"2020062614050317700_B44","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/978-3-030-45257-5_18","article-title":"PaccMannRL: Designing anticancer drugs from transcriptomic data via reinforcement learning","volume-title":"Research in Computational Molecular Biology (RECOMB) Proceedings 24","author":"Born","year":"2020"}],"container-title":["Nucleic Acids Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/nar\/article-pdf\/48\/W1\/W502\/33433085\/gkaa327.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/nar\/article-pdf\/48\/W1\/W502\/33433085\/gkaa327.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T06:44:14Z","timestamp":1593240254000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/nar\/article\/48\/W1\/W502\/5836770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,13]]},"references-count":44,"journal-issue":{"issue":"W1","published-online":{"date-parts":[[2020,5,13]]},"published-print":{"date-parts":[[2020,7,2]]}},"URL":"https:\/\/doi.org\/10.1093\/nar\/gkaa327","relation":{},"ISSN":["0305-1048","1362-4962"],"issn-type":[{"value":"0305-1048","type":"print"},{"value":"1362-4962","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,7,2]]},"published":{"date-parts":[[2020,5,13]]}}}