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Despite this advancement, the variability in the performance of natural enzymes and the fragmentation and diversity of existing data formats pose significant challenges to researchers. Furthermore, AI-driven enzyme design is limited by the quality and quantity of available data. To address these issues, we introduce the light industrial core enzyme database (LICEDB), the first database dedicated exclusively to managing and standardizing enzymes for light industry applications. LICEDB, with its integrated modules for data retrieval, similarity analysis, and structural analysis, will enhance the efficient industrial application of enzymes and strengthen AI-driven predictive research, thereby advancing data sharing and utilization in the field of enzyme innovation.<\/jats:p>\n               <jats:p>Database URL: http:\/\/lujialab.org.cn\/on-line-databases\/<\/jats:p>","DOI":"10.1093\/database\/baaf001","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:44:32Z","timestamp":1740098672000},"source":"Crossref","is-referenced-by-count":3,"title":["LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design"],"prefix":"10.1093","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4072-3521","authenticated-orcid":false,"given":"Lei","family":"Gong","sequence":"first","affiliation":[{"name":"School of Chemistry and Molecular Engineering, East China Normal University , No.500 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