{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:51:07Z","timestamp":1776891067369,"version":"3.51.2"},"reference-count":73,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"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\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFE0206400"],"award-info":[{"award-number":["2021YFE0206400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22220102001"],"award-info":[{"award-number":["22220102001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of China of Zhejiang Province","award":["LD22H300001"],"award-info":[{"award-number":["LD22H300001"]}]},{"name":"Natural Science Foundation of China of Zhejiang Province","award":["LZ19H300001"],"award-info":[{"award-number":["LZ19H300001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A\/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A\/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.<\/jats:p>","DOI":"10.1093\/bib\/bbac592","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T10:54:27Z","timestamp":1672138467000},"source":"Crossref","is-referenced-by-count":9,"title":["Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A\/LSD1) inhibitors"],"prefix":"10.1093","volume":"24","author":[{"given":"Dong","family":"Wang","sequence":"first","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"},{"name":"State Key Lab of CAD&CG, Zhejiang University , Hangzhou 310058 Zhejiang , 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Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"},{"name":"State Key Lab of CAD&CG, Zhejiang University , Hangzhou 310058 Zhejiang , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hucheng","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University , Wuhan 430070 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-116X","authenticated-orcid":false,"given":"De-Xin","family":"Kong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University , Wuhan 430070 , 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