{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:39:41Z","timestamp":1778575181181,"version":"3.51.4"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Portuguese Research Agency FCT","award":["UIDB\/00326\/2020"],"award-info":[{"award-number":["UIDB\/00326\/2020"]}]},{"name":"Deep Drug Discovery and Deployment","award":["CENTRO-01-0145-FEDER-029266"],"award-info":[{"award-number":["CENTRO-01-0145-FEDER-029266"]}]},{"name":"Deep Drug Discovery and Deployment","award":["2021.151089"],"award-info":[{"award-number":["2021.151089"]}]},{"name":"Deep Drug Discovery and Deployment","award":["2021.07538.BD"],"award-info":[{"award-number":["2021.07538.BD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The generation of candidate hit molecules with the potential to be used in cancer treatment is a challenging task. In this context, computational methods based on deep learning have been employed to improve in silico drug design methodologies. Nonetheless, the applied strategies have focused solely on the chemical aspect of the generation of compounds, disregarding the likely biological consequences for the organism\u2019s dynamics. Herein, we propose a method to implement targeted molecular generation that employs biological information, namely, disease-associated gene expression data, to conduct the process of identifying interesting hits. When applied to the generation of USP7 putative inhibitors, the framework managed to generate promising compounds, with more than 90% of them containing drug-like properties and essential active groups for the interaction with the target. Hence, this work provides a novel and reliable method for generating new promising compounds focused on the biological context of the disease.<\/jats:p>","DOI":"10.1093\/bib\/bbac270","type":"journal-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T05:47:02Z","timestamp":1657000022000},"source":"Crossref","is-referenced-by-count":13,"title":["Deep generative model for therapeutic targets using transcriptomic disease-associated data\u2014USP7 case study"],"prefix":"10.1093","volume":"23","author":[{"given":"Tiago","family":"Pereira","sequence":"first","affiliation":[{"name":"Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering , Univ Coimbra, Coimbra, Portugal"}]},{"given":"Maryam","family":"Abbasi","sequence":"additional","affiliation":[{"name":"Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering , Univ Coimbra, Coimbra, Portugal"}]},{"given":"Rita I","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Laboratory of Pharmaceutical Chemistry Faculty of Pharmacy, Univ Coimbra , Coimbra, Portugal"},{"name":"Center for Neuroscience and Cell Biology Center for Innovative Biomedicine and Biotechnology, Univ Coimbra , Coimbra, Portugal"}]},{"given":"Romina A","family":"Guedes","sequence":"additional","affiliation":[{"name":"Laboratory of Pharmaceutical Chemistry Faculty of Pharmacy, Univ Coimbra , Coimbra, Portugal"},{"name":"Center for Neuroscience and Cell Biology Center for Innovative Biomedicine and Biotechnology, Univ Coimbra , Coimbra, Portugal"}]},{"given":"Jorge A R","family":"Salvador","sequence":"additional","affiliation":[{"name":"Laboratory of Pharmaceutical Chemistry Faculty of Pharmacy, Univ Coimbra , Coimbra, Portugal"},{"name":"Center for Neuroscience and Cell Biology Center for Innovative Biomedicine and Biotechnology, Univ Coimbra , Coimbra, Portugal"}]},{"given":"Joel P","family":"Arrais","sequence":"additional","affiliation":[{"name":"Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering , Univ Coimbra, Coimbra, Portugal"}]}],"member":"286","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"2022071906185192700_ref1","article-title":"Quantifying the chemical beauty of drugs","volume":"4","author":"Richard Bickerton","year":"2012","journal-title":"Nat Chem"},{"key":"2022071906185192700_ref2","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2021.102269","article-title":"Paccmannrl: de novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning","volume":"24","author":"Born","year":"2021","journal-title":"iScience"},{"key":"2022071906185192700_ref3","doi-asserted-by":"crossref","DOI":"10.1158\/2159-8290.CD-16-0611","article-title":"Adaptive reprogramming of de novo pyrimidine synthesis is a metabolic vulnerability in triple-negative breast cancer","volume":"7","author":"Brown","year":"2017","journal-title":"Cancer Discov"},{"key":"2022071906185192700_ref4","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btn186","article-title":"A maximum common substructure-based algorithm for searching and predicting drug-like compounds","volume":"24","author":"Cao","year":"2008","journal-title":"Bioinformatics"},{"key":"2022071906185192700_ref5","article-title":"Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets","volume":"8","author":"Chen","year":"2017","journal-title":"Nat Commun"},{"issue":"6","key":"2022071906185192700_ref6","article-title":"Molecular design in drug discovery: a comprehensive review of deep generative models","volume":"22","author":"Yu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022071906185192700_ref7","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2020.00733","article-title":"Discovering anti-cancer drugs via computational methods","volume":"11","author":"Cui","year":"2020","journal-title":"Front Pharmacol"},{"key":"2022071906185192700_ref8","article-title":"Antitumor effects of artesunate on human breast carcinoma mcf-7 cells and igf-ir expression in nude mice xenografts","volume":"26","author":"Dong","year":"2014","journal-title":"Chin J Cancer Res"},{"issue":"1","key":"2022071906185192700_ref9","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab391","article-title":"Deep learning in retrosynthesis planning: datasets, models and tools","volume":"23","author":"Dong","year":"2022","journal-title":"Brief Bioinform"},{"key":"2022071906185192700_ref10","article-title":"Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions","volume":"1","author":"Ertl","year":"2009","journal-title":"J Chem"},{"key":"2022071906185192700_ref11","doi-asserted-by":"crossref","DOI":"10.1080\/15384101.2017.1288326","article-title":"The importance of regulatory ubiquitination in cancer and metastasis","volume":"16","author":"Gallo","year":"2017","journal-title":"Cell Cycle"},{"key":"2022071906185192700_ref12","first-page":"2017","article-title":"The chembl database in 2017","volume":"45","author":"Gaulton","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"2022071906185192700_ref13","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10549-021-06367-5","article-title":"A randomized, placebo-controlled phase 2 study of paclitaxel in combination with reparixin compared to paclitaxel alone as front-line therapy for metastatic triple-negative breast cancer (frida)","volume":"190","author":"Goldstein","year":"2021","journal-title":"Breast Cancer Res Treat"},{"key":"2022071906185192700_ref14","doi-asserted-by":"crossref","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Central Sci"},{"key":"2022071906185192700_ref15","doi-asserted-by":"crossref","DOI":"10.1007\/s10458-022-09552-y","article-title":"A practical guide to multi-objective reinforcement learning and planning","volume-title":"Auton Agent Multi Agent Syst","author":"Hayes"},{"issue":"8","key":"2022071906185192700_ref16","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2022071906185192700_ref17","doi-asserted-by":"crossref","DOI":"10.1021\/acsomega.0c01149","article-title":"Generative model for proposing drug candidates satisfying anticancer properties using a conditional variational autoencoder","volume":"5","author":"Joo","year":"2020","journal-title":"ACS Omega"},{"key":"2022071906185192700_ref18","doi-asserted-by":"crossref","DOI":"10.1021\/acs.molpharmaceut.7b00346","article-title":"Drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico","volume":"14","author":"Kadurin","year":"2017","journal-title":"Mol Pharm"},{"key":"2022071906185192700_ref19","doi-asserted-by":"crossref","DOI":"10.1561\/2200000056","article-title":"An introduction to variational autoencoders","volume":"12","author":"Kingma","year":"2019","journal-title":"Found Trends Mach Learn"},{"issue":"5795","key":"2022071906185192700_ref20","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1126\/science.1132939","article-title":"The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease","volume":"313","author":"Lamb","year":"2006","journal-title":"Science"},{"key":"2022071906185192700_ref21","doi-asserted-by":"crossref","DOI":"10.1038\/sj.cdd.4401910","article-title":"The p53 pathway: what questions remain to be explored?","volume":"13","author":"Levine","year":"2006","journal-title":"Cell Death Differ"},{"key":"2022071906185192700_ref22","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-019-13807-w","article-title":"De novo generation of hit-like molecules from gene expression signatures using artificial intelligence","volume":"11","author":"M\u00e9ndez-Lucio","year":"2020","journal-title":"Nat Commun"},{"key":"2022071906185192700_ref23","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/8427042","article-title":"Application of computational biology and artificial intelligence technologies in cancer precision drug discovery","volume":"2019","author":"Nagarajan","year":"2019","journal-title":"Biomed Res Int"},{"key":"2022071906185192700_ref24","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.103915","article-title":"A multi-objective deep reinforcement learning framework","volume":"96","author":"Nguyen","year":"2020","journal-title":"Eng Appl Artif Intel"},{"key":"2022071906185192700_ref25","doi-asserted-by":"crossref","DOI":"10.1038\/sj.onc.1207371","article-title":"Ubiquitin and breast cancer","volume":"23","author":"Ohta","year":"2004","journal-title":"Oncogene"},{"key":"2022071906185192700_ref26","article-title":"Molecular de-novo design through deep reinforcement learning","volume":"9","author":"Olivecrona","year":"2017","journal-title":"J Chem"},{"key":"2022071906185192700_ref27","article-title":"Diversity oriented deep reinforcement learning for targeted molecule generation","volume":"13","author":"Pereira","year":"2021","journal-title":"J Chem"},{"issue":"6400","key":"2022071906185192700_ref28","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1126\/science.aat2663","article-title":"Inverse molecular design using machine learning: generative models for matter engineering","volume":"361","author":"Sanchez-Lengeling","year":"2018","journal-title":"Science"},{"key":"2022071906185192700_ref29","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/CBMS52027.2021.00067","volume-title":"2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)","author":"Santos","year":"2021"},{"key":"2022071906185192700_ref30","doi-asserted-by":"crossref","DOI":"10.1158\/1535-7163.MCT-14-0778","article-title":"Histone deacetylase inhibitor entinostat inhibits tumor-initiating cells in triple-negative breast cancer cells","volume":"14","author":"Schech","year":"2015","journal-title":"Mol Cancer Ther"},{"key":"2022071906185192700_ref31","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-020-17844-8","article-title":"Machine learning for chemical discovery","volume":"11","author":"Tkatchenko","year":"2020","journal-title":"Nat Commun"},{"key":"2022071906185192700_ref32","doi-asserted-by":"crossref","DOI":"10.1080\/13543784.2017.1353077","article-title":"Entinostat for the treatment of breast cancer","volume":"26","author":"Trapani","year":"2017","journal-title":"Expert Opin Investig Drugs"},{"key":"2022071906185192700_ref33","article-title":"Usp7: novel drug target in cancer therapy","volume":"10","author":"Wang","year":"2019","journal-title":"Front Pharmacol"},{"issue":"1","key":"2022071906185192700_ref34","doi-asserted-by":"crossref","DOI":"10.1186\/s13058-020-01296-5","article-title":"Triple-negative breast cancer molecular subtyping and treatment progress","volume":"22","author":"Yin","year":"2020","journal-title":"Breast Cancer Res"},{"key":"2022071906185192700_ref35","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2017.08.010","article-title":"From machine learning to deep learning: progress in machine intelligence for rational drug discovery","volume":"22","author":"Zhang","year":"2017","journal-title":"Drug Discov Today"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/4\/bbac270\/45017255\/bbac270.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/4\/bbac270\/45017255\/bbac270.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T06:20:39Z","timestamp":1658211639000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac270\/6628785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,5]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7,18]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac270","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,7,18]]},"published":{"date-parts":[[2022,7,5]]},"article-number":"bbac270"}}