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How to improve the performance of the DILI prediction model to accurately predict liver injury was an urgent problem for researchers in the field of medical research. In order to solve this scientific problem, this research collected a comprehensive and accurate dataset of DILI with high recognition and high quality based on clinically confirmed DILI compound datasets, including 1446 chemical compounds. Then, the residual neural network with 18-layer by using more 5-layer blocks (ResNet18) with deep neural network (ResNet18DNN) model was proposed to predict DILI, which was an improved model for DILI prediction through vectorization of compound structure image. In predicting DILI, the ResNet18DNN learned greatly and outperformed the existing state-of-the-art DILI predictors. The results of DILI prediction model based on ResNet18DNN showed that the AUC (area under the curve), accuracy, recall, precision, F1-score and specificity of the training set were 0.973, 0.992, 0.995, 0.994, 0.995 and 0.975; those of test set were, respectively, 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913, which were better than the performance of previously published described DILI prediction models. This method adopted ResNet18 embedding method to vectorize molecular structure images and the evaluation indicators of Resnet18DNN were obtained after 10\u00a0000 iterations. This prediction approach will greatly improve the performance of the predictive model of DILI and provide an accurate and precise early warning method for DILI in drug development and clinical medication.<\/jats:p>","DOI":"10.1093\/bib\/bbab503","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T12:15:48Z","timestamp":1636114548000},"source":"Crossref","is-referenced-by-count":100,"title":["ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3911-1125","authenticated-orcid":false,"given":"Zhao","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruijin","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongcai","family":"Shang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Chinese 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