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While many computational methods have been developed for such predictions and have yielded promising results, two challenges persist: (i) The cold start problem of new drugs, which increases the difficulty of prediction due to lack of historical data or prior knowledge. (ii) The vastness of the compound search space for potential drug candidates. In this study, we present a promising method that not only enhances the accuracy of identifying potential novel drug candidates but also refines the search space. Drawing inspiration from solutions to the cold start problem in recommender systems, we apply \u2018learning to rank\u2019 techniques to the field of new drug discovery. Furthermore, we propose using three similarity metrics to condense the compound search space into compact yet high-quality spaces, allowing for more efficient screening of potential drug candidates. Experimental results from two widely used datasets demonstrate that our method outperforms other state-of-the-art approaches in the new drug cold-start scenario. Additionally, we have verified that it is feasible to identify potential drug candidates within these high-quality compound search spaces. To our knowledge, this study is the first to address drug cold-start problem in such a confined space, potentially providing valuable insights and guidance for drug screening.<\/jats:p>","DOI":"10.1093\/bib\/bbaf024","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T12:01:44Z","timestamp":1737720104000},"source":"Crossref","is-referenced-by-count":2,"title":["Identify potential drug candidates within a high-quality compound search space"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2968-6435","authenticated-orcid":false,"given":"Xiaoqing","family":"Ru","sequence":"first","affiliation":[{"name":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital , No. 100, Minjiang Avenue, Smart New Town, Quzhou, Zhejiang Province, 324000 ,","place":["China"]},{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , No. 1, Chengdian Road, 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