{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:01:22Z","timestamp":1775815282154,"version":"3.50.1"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T00:00:00Z","timestamp":1579219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872216"],"award-info":[{"award-number":["61872216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472205"],"award-info":[{"award-number":["61472205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81630103"],"award-info":[{"award-number":["81630103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhongguancun Haihua Institute for Frontier Information Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Quantitative structure\u2013activity relationship (QSAR) and drug\u2013target interaction (DTI) prediction are both commonly used in drug discovery. Collaboration among pharmaceutical institutions can lead to better performance in both QSAR and DTI prediction. However, the drug-related data privacy and intellectual property issues have become a noticeable hindrance for inter-institutional collaboration in drug discovery.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We have developed two novel algorithms under secure multiparty computation (MPC), including QSARMPC and DTIMPC, which enable pharmaceutical institutions to achieve high-quality collaboration to advance drug discovery without divulging private drug-related information. QSARMPC, a neural network model under MPC, displays good scalability and performance and is feasible for privacy-preserving collaboration on large-scale QSAR prediction. DTIMPC integrates drug-related heterogeneous network data and accurately predicts novel DTIs, while keeping the drug information confidential. Under several experimental settings that reflect the situations in real drug discovery scenarios, we have demonstrated that DTIMPC possesses significant performance improvement over the baseline methods, generates novel DTI predictions with supporting evidence from the literature and shows the feasible scalability to handle growing DTI data. All these results indicate that QSARMPC and DTIMPC can provide practically useful tools for advancing privacy-preserving drug discovery.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The source codes of QSARMPC and DTIMPC are available on the GitHub: https:\/\/github.com\/rongma6\/QSARMPC_DTIMPC.git.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa038","type":"journal-article","created":{"date-parts":[[2020,1,15]],"date-time":"2020-01-15T12:10:58Z","timestamp":1579090258000},"page":"2872-2880","source":"Crossref","is-referenced-by-count":27,"title":["Secure multiparty computation for privacy-preserving drug discovery"],"prefix":"10.1093","volume":"36","author":[{"given":"Rong","family":"Ma","sequence":"first","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences , Tsinghua University, Beijing 100084, China"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences , Tsinghua University, Beijing 100084, China"}]},{"given":"Chenxing","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences , Tsinghua University, Beijing 100084, China"}]},{"given":"Fangping","family":"Wan","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences , Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5768-4437","authenticated-orcid":false,"given":"Hailin","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Medicine , Tsinghua University, Beijing 100084, China"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences , Tsinghua University, Beijing 100084, China"}]},{"given":"Jianyang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences , Tsinghua University, Beijing 100084, China"},{"name":"MOE Key Laboratory of Bioinformatics , Tsinghua University, Beijing 100084, China"}]}],"member":"286","published-online":{"date-parts":[[2020,1,17]]},"reference":[{"key":"2023013111480641300_btaa038-B1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/978-3-540-36266-1_10","volume-title":"Applications of Soft Computing","author":"Barrett","year":"2006"},{"key":"2023013111480641300_btaa038-B2","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1093\/bioinformatics\/btp433","article-title":"Supervised prediction of drug-target interactions using bipartite local models","volume":"25","author":"Bleakley","year":"2009","journal-title":"Bioinformatics"},{"key":"2023013111480641300_btaa038-B3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0097-8485(01)00094-8","article-title":"Drug design by machine learning: support vector machines for pharmaceutical data analysis","volume":"26","author":"Burbidge","year":"2001","journal-title":"Comput. Chem"},{"key":"2023013111480641300_btaa038-B4","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/0893-133X(94)00129-N","article-title":"Radioreceptor binding profile of the atypical antipsychotic olanzapine","volume":"14","author":"Bymaster","year":"1996","journal-title":"Neuropsychopharmacology"},{"key":"2023013111480641300_btaa038-B5","first-page":"402","author":"Caruana","year":"2001"},{"key":"2023013111480641300_btaa038-B6","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1093\/bioinformatics\/btw758","article-title":"Princess: privacy-protecting rare disease international network collaboration via encryption through software guard extensions","volume":"33","author":"Chen","year":"2016","journal-title":"Bioinformatics"},{"key":"2023013111480641300_btaa038-B7","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/nbt.4108","article-title":"Secure genome-wide association analysis using multiparty computation","volume":"36","author":"Cho","year":"2018","journal-title":"Nat. Biotechnol"},{"key":"2023013111480641300_btaa038-B8","doi-asserted-by":"crossref","first-page":"D1104","DOI":"10.1093\/nar\/gks994","article-title":"The comparative toxicogenomics database: update 2013","volume":"41","author":"Davis","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023013111480641300_btaa038-B9","author":"Fredrikson","year":"2015"},{"key":"2023013111480641300_btaa038-B10","doi-asserted-by":"crossref","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","article-title":"ChEMBL: a large-scale bioactivity database for drug discovery","volume":"40","author":"Gaulton","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023013111480641300_btaa038-B11","doi-asserted-by":"crossref","first-page":"4289","DOI":"10.2174\/092986712802884259","article-title":"Machine learning techniques and drug design","volume":"19","author":"Gertrudes","year":"2012","journal-title":"Curr. Med. Chem"},{"key":"2023013111480641300_btaa038-B12","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1126\/science.aat4807","article-title":"Realizing private and practical pharmacological collaboration","volume":"362","author":"Hie","year":"2018","journal-title":"Science"},{"key":"2023013111480641300_btaa038-B13","first-page":"603","author":"Hitaj","year":"2017"},{"key":"2023013111480641300_btaa038-B14","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1126\/science.aam9710","article-title":"Deriving genomic diagnoses without revealing patient genomes","volume":"357","author":"Jagadeesh","year":"2017","journal-title":"Science"},{"key":"2023013111480641300_btaa038-B15","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s10822-005-9011-5","article-title":"Secure analysis of distributed chemical databases without data integration","volume":"19","author":"Karr","year":"2005","journal-title":"J. Comput. Aided Mol. Des"},{"key":"2023013111480641300_btaa038-B16","doi-asserted-by":"crossref","first-page":"11322","DOI":"10.1073\/pnas.89.23.11322","article-title":"Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase","volume":"89","author":"King","year":"1992","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023013111480641300_btaa038-B17","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1111\/gtc.12022","article-title":"Activity-based kinase profiling of approved tyrosine kinase inhibitors","volume":"18","author":"Kitagawa","year":"2013","journal-title":"Genes Cells"},{"key":"2023013111480641300_btaa038-B18","first-page":"D1035","article-title":"DrugBank 3.0: a comprehensive resource for \u2018omics\u2019 research on drugs","volume":"39(suppl_1","author":"Knox","year":"2010","journal-title":"Nucleic Acids Res"},{"key":"2023013111480641300_btaa038-B19"},{"key":"2023013111480641300_btaa038-B20","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.drudis.2014.10.012","article-title":"Machine-learning approaches in drug discovery: methods and applications","volume":"20","author":"Lavecchia","year":"2015","journal-title":"Drug Discov. Today"},{"key":"2023013111480641300_btaa038-B21","first-page":"1299","author":"Li","year":"2019"},{"key":"2023013111480641300_btaa038-B22","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat. Commun"},{"key":"2023013111480641300_btaa038-B23","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/ci500747n","article-title":"Deep neural nets as a method for quantitative structure-activity relationships","volume":"55","author":"Ma","year":"2015","journal-title":"J. Chem. Inf. Model"},{"key":"2023013111480641300_btaa038-B24","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1093\/bioinformatics\/bts670","article-title":"Drug-target interaction prediction by learning from local information and neighbors","volume":"29","author":"Mei","year":"2013","journal-title":"Bioinformatics"},{"key":"2023013111480641300_btaa038-B25","first-page":"35","author":"Mohassel","year":"2018"},{"key":"2023013111480641300_btaa038-B26","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/nchembio.576","article-title":"An active role for machine learning in drug development","volume":"7","author":"Murphy","year":"2011","journal-title":"Nat. Chem. Biol"},{"key":"2023013111480641300_btaa038-B27","first-page":"807","author":"Nair","year":"2010"},{"key":"2023013111480641300_btaa038-B28","doi-asserted-by":"crossref","first-page":"i60","DOI":"10.1093\/bioinformatics\/btu269","article-title":"Inductive matrix completion for predicting gene-disease associations","volume":"30","author":"Natarajan","year":"2014","journal-title":"Bioinformatics"},{"key":"2023013111480641300_btaa038-B29","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611971163","volume-title":"The Symmetric Eigenvalue Problem","author":"Parlett","year":"1998"},{"key":"2023013111480641300_btaa038-B30","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"2023013111480641300_btaa038-B31","first-page":"1","author":"Schunter","year":"2016"},{"key":"2023013111480641300_btaa038-B32","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1177\/0269881107082944","article-title":"Asenapine: a novel psychopharmacologic agent with a unique human receptor signature","volume":"23","author":"Shahid","year":"2009","journal-title":"J. Psychopharmacol"},{"key":"2023013111480641300_btaa038-B33","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1145\/359168.359176","article-title":"How to share a secret","volume":"22","author":"Shamir","year":"1979","journal-title":"Commun. ACM"},{"key":"2023013111480641300_btaa038-B34","first-page":"1310","author":"Shokri","year":"2015"},{"key":"2023013111480641300_btaa038-B35","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/0022-2836(81)90087-5","article-title":"Identification of common molecular subsequences","volume":"147","author":"Smith","year":"1981","journal-title":"J. Mol. Biol"},{"key":"2023013111480641300_btaa038-B36","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res"},{"key":"2023013111480641300_btaa038-B37","first-page":"1139","author":"Sutskever","year":"2013"},{"key":"2023013111480641300_btaa038-B38","doi-asserted-by":"crossref","first-page":"D380","DOI":"10.1093\/nar\/gkv1277","article-title":"STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data","volume":"44","author":"Szklarczyk","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023013111480641300_btaa038-B39","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1002\/minf.201600073","article-title":"BIGCHEM: challenges and opportunities for big data analysis in chemistry","volume":"35","author":"Tetko","year":"2016","journal-title":"Mol. Inf"},{"key":"2023013111480641300_btaa038-B40","first-page":"613","author":"Tong","year":"2006"},{"key":"2023013111480641300_btaa038-B41","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1111\/j.1365-2141.2011.08685.x","article-title":"BAY 43-9006\/Sorafenib blocks CSF1R activity and induces apoptosis in various classical Hodgkin lymphoma cell lines","volume":"155","author":"Ullrich","year":"2011","journal-title":"Br. J. Haematol"},{"key":"2023013111480641300_btaa038-B42","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.1093\/nar\/gky092","article-title":"UniProt: the universal protein knowledgebase","volume":"46","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023013111480641300_btaa038-B43","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","article-title":"Gaussian interaction profile kernels for predicting drug-target interaction","volume":"27","author":"Van Laarhoven","year":"2011","journal-title":"Bioinformatics"},{"key":"2023013111480641300_btaa038-B44","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1093\/bioinformatics\/bty543","article-title":"NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions","volume":"35","author":"Wan","year":"2019","journal-title":"Bioinformatics"},{"key":"2023013111480641300_btaa038-B45","doi-asserted-by":"crossref","first-page":"i357","DOI":"10.1093\/bioinformatics\/btv260","article-title":"Exploiting ontology graph for predicting sparsely annotated gene function","volume":"31","author":"Wang","year":"2015","journal-title":"Bioinformatics"},{"key":"2023013111480641300_btaa038-B46","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1093\/bioinformatics\/btu403","article-title":"Drug repositioning by integrating target information through a heterogeneous network model","volume":"30","author":"Wang","year":"2014","journal-title":"Bioinformatics"},{"key":"2023013111480641300_btaa038-B47","first-page":"123","author":"Xia","year":"2009"},{"key":"2023013111480641300_btaa038-B48","doi-asserted-by":"crossref","first-page":"S6","DOI":"10.1186\/1752-0509-4-S2-S6","article-title":"Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces","volume":"4","author":"Xia","year":"2010","journal-title":"BMC Syst. Biol"},{"key":"2023013111480641300_btaa038-B49","first-page":"160","author":"Yao","year":"1982"},{"key":"2023013111480641300_btaa038-B50","first-page":"593","author":"Yu","year":"2014"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa038\/32648930\/btaa038.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/9\/2872\/48984116\/bioinformatics_36_9_2872.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/9\/2872\/48984116\/bioinformatics_36_9_2872.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T04:15:53Z","timestamp":1695615353000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/9\/2872\/5709032"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,1,17]]},"references-count":50,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2020,5,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa038","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,5,1]]},"published":{"date-parts":[[2020,1,17]]}}}