PROTAC-DB 4.0: an expanded knowledgebase of targeted protein degradation modalities with AI-powered knowledge discovery

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ID: 337865
2026
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Abstract
Proteolysis-targeting chimeras (PROTACs) represent an emerging therapeutic modality that harnesses the ubiquitin-proteasome system to induce targeted protein degradation (TPD). Their event-driven and catalytic mechanisms provide new opportunities to target previously undruggable proteins. However, the rapid expansion of TPD technologies, including molecular glues (MGs) and X-functional targeting chimeras (XTACs), together with the increasing application of artificial intelligence (AI) in degrader design, highlights the need for comprehensive and continuously updated data resources. Here, we present PROTAC-DB 4.0, an expanded knowledgebase covering major targeted degradation and induced-proximity technologies. Compared with the previous version, PROTAC-DB 4.0 contains 15 502 PROTAC entries, together with 24 798 MG entries and 272 XTAC entries, with expanded experimental annotations, patent-derived compounds, and modality-specific data fields. A major advancement in PROTAC-DB 4.0 is the integration of a large language model and retrieval-augmented generation-based natural language query system, which connects structured database records with scientific literature to enable intuitive knowledge discovery across PROTACs, MGs, and XTACs. By combining comprehensive data curation with AI-assisted information retrieval, PROTAC-DB 4.0 provides an accessible platform to support degrader discovery, data-driven modeling, and rational design of next-generation targeted degradation and induced-proximity therapeutics. PROTAC-DB 4.0 is freely accessible at http://cadd.zju.edu.cn/protacdb/.
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Authors Jingxuan Ge, Chen Yin, Yurong Li, Shi Li, Kexin Xu, Rongfan Tang, Gaoqi Weng, Hao Luo, Lin Tang, Youqiao Qian, Shukai Gu, Chang‐Yu Hsieh, Jian Wu, Dan Li, Huiyong Sun, Tingjun Hou
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Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1098
URL
https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkag1098/72639794/gkag1098.pdf
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