{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:18:03Z","timestamp":1778347083383,"version":"3.51.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T00:00:00Z","timestamp":1639785600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T00:00:00Z","timestamp":1639785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s13721-021-00351-1","type":"journal-article","created":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T03:07:41Z","timestamp":1639796861000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Generating novel molecule for target protein (SARS-CoV-2) using drug\u2013target interaction based on graph neural network"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7940-5865","authenticated-orcid":false,"given":"Amit","family":"Ranjan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivansh","family":"Shukla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepanjan","family":"Datta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajiv","family":"Misra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,12,18]]},"reference":[{"issue":"1","key":"351_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-019-0393-0","volume":"11","author":"J Ar\u00fas-Pous","year":"2019","unstructured":"Ar\u00fas-Pous J, Johansson SV, Prykhodko O, Bjerrum EJ, Tyrchan C, Reymond J-L, Chen H, Engkvist O (2019) Randomized smiles strings improve the quality of molecular generative models. J Cheminform 11(1):1\u201313","journal-title":"J Cheminform"},{"issue":"15","key":"351_CR2","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":"351_CR3","doi-asserted-by":"publisher","first-page":"1700123","DOI":"10.1002\/minf.201700123","volume":"37","author":"T Blaschke","year":"2018","unstructured":"Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H (2018) Application of generative autoencoder in de novo molecular design. Mol Inf 37:1700123","journal-title":"Mol Inf"},{"key":"351_CR4","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1038\/nbt.1990","volume":"29","author":"MI Davis","year":"2011","unstructured":"Davis MI, Hunt JP, Herrgard S, Ciceri P, Wodicka LM, Pallares G, Hocker M, Treiber DK, Zarrinkar PP (2011) Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol 29:1046\u20131051","journal-title":"Nat Biotechnol"},{"issue":"10","key":"351_CR5","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1002\/cmdc.200800178","volume":"3","author":"J Degen","year":"2008","unstructured":"Degen J, Wegscheid-Gerlach C, Zaliani A, Rarey M (2008) On the art of compiling and using \u2018drug-like\u2019 chemical fragment spaces. ChemMedChem 3(10):1503","journal-title":"ChemMedChem"},{"key":"351_CR6","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"JL Durant","year":"2002","unstructured":"Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of mdl keys for use in drug discovery. J Chem Inf Comput Sci 42:1273\u20131280","journal-title":"J Chem Inf Comput Sci"},{"key":"351_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13721-020-00274-3","volume":"10","author":"GIB Janairo","year":"2021","unstructured":"Janairo GIB, Yu DEC, Janairo JIB (2021) A machine learning regression model for the screening and design of potential sars-cov-2 protease inhibitors. Netw Model Anal Health Inf Bioinform 10:1\u20138","journal-title":"Netw Model Anal Health Inf Bioinform"},{"key":"351_CR8","doi-asserted-by":"publisher","first-page":"20701","DOI":"10.1039\/D0RA02297G","volume":"10","author":"M Jiang","year":"2020","unstructured":"Jiang M, Li Z, Zhang S, Wang S, Wang X, Yuan Q, Wei Z (2020) Drug-target affinity prediction using graph neural network and contact maps. RSC Adv 10:20701\u201320712","journal-title":"RSC Adv"},{"key":"351_CR9","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3390\/molecules26010039","volume":"26","author":"M Khan","year":"2020","unstructured":"Khan M, Adil SF, Alkhathlan HZ, Tahir MN, Saif S, Khan M, Khan ST (2020) Covid-19: a global challenge with old history, epidemiology and progress so far. Molecules 26:39","journal-title":"Molecules"},{"key":"351_CR10","unstructured":"Li Y, Tarlow D, Brockschmidt M, Zemel R (2015) Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493"},{"key":"351_CR11","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","volume":"37","author":"T Nguyen","year":"2021","unstructured":"Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S (2021a) Graphdta: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37:1140\u20131147","journal-title":"Bioinformatics"},{"key":"351_CR12","doi-asserted-by":"publisher","unstructured":"Nguyen TM, Nguyen T, Le TM, Tran T (2021b) GEFA: Early fusion approach in drug-target affinity prediction. In: IEEE\/ACM transactions on computational biology and bioinformatics 1. https:\/\/doi.org\/10.1109\/tcbb.2021.3094217","DOI":"10.1109\/tcbb.2021.3094217"},{"key":"351_CR13","doi-asserted-by":"crossref","unstructured":"\u00d6zt\u00fcrk H, \u00d6zg\u00fcr A, Ozkirimli E (2018) Deepdta: deep drug\u2013target binding affinity prediction. Bioinformatics 34","DOI":"10.1093\/bioinformatics\/bty593"},{"key":"351_CR14","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.3389\/fphar.2020.565644","volume":"11","author":"D Polykovskiy","year":"2020","unstructured":"Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, Golovanov S, Tatanov O, Belyaev S, Kurbanov R, Artamonov A, Aladinskiy V, Veselov M, Kadurin A, Johansson S, Chen H, Nikolenko S, Aspuru-Guzik A, Zhavoronkov A (2020) Molecular sets (moses): a benchmarking platform for molecular generation models. Front Pharmacol 11:1931","journal-title":"Front Pharmacol"},{"key":"351_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-019-0397-9","volume":"11","author":"O Prykhodko","year":"2019","unstructured":"Prykhodko O, Johansson SV, Kotsias P-C, Ar\u00fas-Pous J, Bjerrum EJ, Engkvist O, Chen H (2019) A de novo molecular generation method using latent vector based generative adversarial network. J Cheminform 11:1\u201313","journal-title":"J Cheminform"},{"key":"351_CR16","first-page":"9689","volume":"32","author":"R Rao","year":"2019","unstructured":"Rao R, Bhattacharya N, Thomas N, Duan Y, Chen X, Canny J, Abbeel P, Song YS (2019) Evaluating protein transfer learning with tape. Adv Neural Inf Process Syst 32:9689","journal-title":"Adv Neural Inf Process Syst"},{"key":"351_CR17","doi-asserted-by":"publisher","unstructured":"Sanchez-Lengeling B, Outeiral C, Guimaraes GL, Aspuru-Guzik A (2017) Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC). https:\/\/doi.org\/10.26434\/chemrxiv.5309668.v2","DOI":"10.26434\/chemrxiv.5309668.v2"},{"key":"351_CR18","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1038\/s41586-019-1923-7","volume":"577","author":"AW Senior","year":"2020","unstructured":"Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, \u017d\u00eddek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D (2020) Improved protein structure prediction using potentials from deep learning. Nature 577:706\u2013710","journal-title":"Nature"},{"key":"351_CR19","doi-asserted-by":"publisher","first-page":"2305","DOI":"10.1099\/vir.0.19424-0","volume":"84","author":"V Thiel","year":"2003","unstructured":"Thiel V, Ivanov KA, Putics \u00c1, Hertzig T, Schelle B, Bayer S, Wei\u00dfbrich B, Snijder EJ, Rabenau H, Doerr HW, Gorbalenya AE, Ziebuhr J (2003) Mechanisms and enzymes involved in sars coronavirus genome expression. J Gen Virol 84:2305\u20132315","journal-title":"J Gen Virol"},{"key":"351_CR20","doi-asserted-by":"publisher","first-page":"e1005324","DOI":"10.1371\/journal.pcbi.1005324","volume":"13","author":"S Wang","year":"2017","unstructured":"Wang S, Sun S, Li Z, Zhang R, Xu J (2017) Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput Biol 13:e1005324","journal-title":"PLoS Comput Biol"},{"key":"351_CR21","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1001\/jama.2020.1166","volume":"323","author":"OJ Wouters","year":"2020","unstructured":"Wouters OJ, McKee M, Luyten J (2020) Estimated research and development investment needed to bring a new medicine to market, 2009\u20132018. JAMA 323:844\u2013853","journal-title":"JAMA"},{"issue":"13","key":"351_CR22","doi-asserted-by":"publisher","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","volume":"24","author":"Y Yamanishi","year":"2008","unstructured":"Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13):i232\u2013i240","journal-title":"Bioinformatics"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-021-00351-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13721-021-00351-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-021-00351-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T07:46:11Z","timestamp":1670053571000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13721-021-00351-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,18]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["351"],"URL":"https:\/\/doi.org\/10.1007\/s13721-021-00351-1","relation":{},"ISSN":["2192-6662","2192-6670"],"issn-type":[{"value":"2192-6662","type":"print"},{"value":"2192-6670","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,18]]},"assertion":[{"value":"16 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Amit Ranjan, Shivansh Shukla, Deepanjan Datta, and Rajiv Misra announce that they all have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Each individual involved in the research gave their informed consent.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"6"}}