{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T21:23:34Z","timestamp":1781904214656,"version":"3.54.5"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:00:00Z","timestamp":1720396800000},"content-version":"vor","delay-in-days":46,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3600902"],"award-info":[{"award-number":["2022YFC3600902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.<\/jats:p>","DOI":"10.1093\/bib\/bbae321","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T12:33:53Z","timestamp":1720442033000},"source":"Crossref","is-referenced-by-count":58,"title":["DrugMetric: quantitative drug-likeness scoring based on chemical space distance"],"prefix":"10.1093","volume":"25","author":[{"given":"Bowen","family":"Li","sequence":"first","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang , China"},{"name":"College of Electrical and Information Engineering, Hunan University , Changsha, 410082 Hunan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang , China"},{"name":"Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences , Hangzhou, 310024 Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanxin","family":"Tao","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chulin","family":"Sha","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018 Zhejiang , 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