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Computational drug discovery is an effective way to solve such problems. The existence of various large open databases has mitigated the time and resource consumption of traditional drug development and improved the speed of drug discovery. However, the diversity of cell lines used in various databases remains limited, and previous drug discovery methods are ineffective for cross-cell prediction. In this study, we propose a correlation-dependent connectivity map (CDCM) to achieve cross-cell predictions of drug similarity. The CDCM mainly identifies drug\u2013drug or disease\u2013drug relationships from the perspective of gene networks by exploring the correlation changes between genes and identifying similarities in the effects of drugs or diseases on gene expression. We validated the CDCM on multiple datasets and found that it performed well for drug identification across cell lines. A comparison with the Connectivity Map revealed that our method was more stable and performed better across different cell lines. In the application of the CDCM to COVID-19 and MPXV data, the predictions of potential therapeutic compounds for COVID-19 were consistent with several previous studies, and most of the predicted drugs were found to be experimentally effective against MPXV. This result confirms the practical value of the CDCM. With the ability to predict across cell lines, the CDCM outperforms the Connectivity Map, and it has wider application prospects and a reduced cost of use.<\/jats:p>","DOI":"10.1093\/bib\/bbae659","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:34:44Z","timestamp":1734654884000},"source":"Crossref","is-referenced-by-count":4,"title":["CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases"],"prefix":"10.1093","volume":"26","author":[{"given":"Junlei","family":"Liao","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics , HNP-LAMA, , Changsha 410083, Hunan ,","place":["China"]},{"name":"Central South University , HNP-LAMA, , Changsha 410083, Hunan ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2409-2026","authenticated-orcid":false,"given":"Hongyang","family":"Yi","sequence":"additional","affiliation":[{"name":"National Clinical Research Centre for Infectious Diseases, The Third People\u2019s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology , Shenzhen 518112 ,","place":["China"]}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Maternal-Fetal Medicine Institute , Department of Obstetrics and Gynaecology,"},{"name":"Shenzhen Baoan Women\u2019s and Children\u2019s Hospital, Shenzhen 518133, China , Department of Obstetrics and Gynaecology,"}]},{"given":"Sumei","family":"Yang","sequence":"additional","affiliation":[{"name":"National Clinical Research Centre for Infectious Diseases, The Third People\u2019s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology , Shenzhen 518112 ,","place":["China"]}]},{"given":"Duanmei","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics , HNP-LAMA, , Changsha 410083, Hunan ,","place":["China"]},{"name":"Central South University , HNP-LAMA, , Changsha 410083, Hunan ,","place":["China"]}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[{"name":"Maternal-Fetal Medicine Institute , Department of Obstetrics and Gynaecology,"},{"name":"Shenzhen Baoan Women\u2019s and Children\u2019s Hospital, Shenzhen 518133, China , Department of Obstetrics and Gynaecology,"}]},{"given":"Mingxia","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Clinical Research Centre for Infectious Diseases, The Third People\u2019s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology , Shenzhen 518112 ,","place":["China"]}]},{"given":"Jiayin","family":"Shen","sequence":"additional","affiliation":[{"name":"National Clinical Research Centre for Infectious Diseases, The Third People\u2019s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology , Shenzhen 518112 ,","place":["China"]}]},{"given":"Hongzhou","family":"Lu","sequence":"additional","affiliation":[{"name":"National Clinical Research Centre for Infectious Diseases, The Third People\u2019s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology , Shenzhen 518112 ,","place":["China"]}]},{"given":"Yuanling","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics , HNP-LAMA, , Changsha 410083, Hunan ,","place":["China"]},{"name":"Central South University , HNP-LAMA, , Changsha 410083, Hunan ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"2024122000342470000_ref1","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1016\/S0140-6736(21)02758-6","article-title":"Omicron SARS-CoV-2 variant: A new chapter in the COVID-19 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