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Nevertheless, the rational design and discovery of ADCs remain challenging because the relationship between their quintuple structures and activities is difficult to explore and understand. To address this issue, we first introduce a unified deep learning framework called ADCNet to explore such relationship and help design potential ADCs. The ADCNet highly integrates the protein representation learning language model ESM-2 and small-molecule representation learning language model functional group-based bidirectional encoder representations from transformers to achieve activity prediction through learning meaningful features from antigen and antibody protein sequences of ADC, SMILES strings of linker and payload, and drug-antibody ratio (DAR) value. Based on a carefully designed and manually tailored ADC data set, extensive evaluation results reveal that ADCNet performs best on the test set compared to baseline machine learning models across all evaluation metrics. For example, it achieves an average prediction accuracy of 87.12%, a balanced accuracy of 0.8689, and an area under receiver operating characteristic curve of 0.9293 on the test set. In addition, cross-validation, ablation experiments, and external independent testing results further prove the stability, advancement, and robustness of the ADCNet architecture. For the convenience of the community, we develop the first online platform (https:\/\/ADCNet.idruglab.cn) for the prediction of ADCs activity based on the optimal ADCNet model, and the source code is publicly available at https:\/\/github.com\/idrugLab\/ADCNet.<\/jats:p>","DOI":"10.1093\/bib\/bbaf228","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T09:16:57Z","timestamp":1748337417000},"source":"Crossref","is-referenced-by-count":15,"title":["ADCNet: a unified framework for predicting the activity of antibody-drug conjugates"],"prefix":"10.1093","volume":"26","author":[{"given":"Liye","family":"Chen","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering , Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biaoshun","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering , Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihao","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering , Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology 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International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 510006,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxin","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering , Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, No. 382 Waihuan East Road, Higher Education Mega Center, Guangzhou 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