{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T02:06:14Z","timestamp":1769565974365,"version":"3.49.0"},"reference-count":88,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>DNA-encoded library (DEL) technology has been developed as a powerful platform for drug development. Live cell-based selection methodologies were recently developed to expedite drug candidate discovery with higher biological relevance. Nevertheless, hit characterization is challenged by prominent background signals of cell-based selections. Therefore, automated data processing streamline compatible with noisy sequencing output is highly desirable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Herein, we report an innovative automatic method that enables the most promising hit identification from large quantities of cell-based DEL datasets with improved accuracy and efficiency. This processing workflow is based on a comprehensive unsupervised algorithm incorporating data pre-processing, feature extracting and outlier filtering, descriptor-based classification, similarity score ranking, and active compound prediction. We performed methodology development with two DEL selection datasets targeting insulin receptor (INSR) on live cells, from both \u223c30 million- and 1.033 billion-membered libraries. The automated scheme has demonstrated high consistency with experimental results as well as self-adaptivity to on-cell DEL datasets with varied library scales. Extended methodology application to cellular thrombopoietin receptor (TPOR) further substantiated the algorithmic generalization capability regarding target proteins. Thus, this approach can serve as a widely applicable workflow automatically differentiating hit compounds and thereby facilitates drug development from candidate discovery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The complete datasets, source code, and pre-trained models are made available at https:\/\/doi.org\/10.5281\/zenodo.17452392 and https:\/\/doi.org\/10.5281\/zenodo.17569557.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag001","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:19:24Z","timestamp":1767615564000},"source":"Crossref","is-referenced-by-count":0,"title":["A hybrid unsupervised methodology on artificial intelligence filtering for automatically processing cellular DNA-encoded library (DEL) datasets"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2215-3422","authenticated-orcid":false,"given":"Yiran","family":"Huang","sequence":"first","affiliation":[{"name":"School of Pharmacy, Shenzhen University Medical School, Shenzhen University , Shenzhen 518060,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0191-9185","authenticated-orcid":false,"given":"Xiao","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Computer Science , The University of Hong Kong , Hong Kong SAR, 999077,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8907-6727","authenticated-orcid":false,"given":"Xiaoyu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong , Hong Kong SAR, 999077,","place":["China"]},{"name":"Laboratory for Synthetic Chemistry and Chemical Biology Limited, Health@InnoHK, Innovation and Technology Commission , Hong Kong SAR, 999077,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7159-9522","authenticated-orcid":false,"given":"Feng","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong , Hong Kong SAR, 999077,","place":["China"]},{"name":"Shenzhen NewDEL Biotech Co., Ltd. , Shenzhen, 518110,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3975-8500","authenticated-orcid":false,"given":"Siu Ming","family":"Yiu","sequence":"additional","affiliation":[{"name":"Department of Computer Science , The University of Hong Kong , Hong Kong SAR, 999077,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"2026012708121096600_btag001-B1","doi-asserted-by":"crossref","first-page":"16051","DOI":"10.1021\/acs.jmedchem.3c01471","article-title":"Discovery of a first-in-class small-molecule 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