DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation

2020 ◽  
Author(s):  
Ting Li ◽  
Weida Tong ◽  
Ruth Roberts ◽  
Zhichao Liu ◽  
Shraddha Thakkar
Keyword(s):  
Deep Learning ◽  
Liver Injury ◽  
Drug Induced ◽  
Injury Prediction ◽  
Drug Induced Liver Injury

scholarly journals In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method

2019 ◽  
Vol 20 (17) ◽  
pp. 4106 ◽  
Author(s):  
Wang ◽  
Xiao ◽  
Chen ◽  
Wang
Keyword(s):  
Liver Injury ◽  
Drug Research ◽  
Molecular Descriptors ◽  
Ensemble Classifier ◽  
Key Factors ◽  
Drug Induced ◽  
Molecular Fingerprints ◽  
Injury Prediction ◽  
Drug Induced Liver Injury ◽  
Fold Cross Validation

Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug‐induced liver injury prediction.

scholarly journals Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

2020 ◽  
Vol 8 ◽  
Author(s):  
Ting Li ◽  
Weida Tong ◽  
Ruth Roberts ◽  
Zhichao Liu ◽  
Shraddha Thakkar
Keyword(s):  
Deep Learning ◽  
Liver Injury ◽  
High Throughput ◽  
Permutation Test ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Attrition Rate ◽  
Support Vector ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

In-silico approach for drug induced liver injury prediction: Recent advances

2018 ◽  
Vol 295 ◽  
pp. 288-295 ◽  
Author(s):  
Neha Saini ◽  
Shikha Bakshi ◽  
Sadhna Sharma
Keyword(s):  
Liver Injury ◽  
In Silico ◽  
Drug Induced ◽  
Injury Prediction ◽  
Drug Induced Liver Injury ◽  
Recent Advances

scholarly journals Deep Learning for Drug-Induced Liver Injury

2015 ◽  
Vol 55 (10) ◽  
pp. 2085-2093 ◽  
Author(s):  
Youjun Xu ◽  
Ziwei Dai ◽  
Fangjin Chen ◽  
Shuaishi Gao ◽  
Jianfeng Pei ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Injury ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals In Vitro Drug-Induced Liver Injury Prediction: Criteria Optimization of Efflux Transporter IC50 and Physicochemical Properties

2017 ◽  
Vol 157 (2) ◽  
pp. 487-499 ◽  
Author(s):  
Robert W. Yucha ◽  
Kan He ◽  
Qin Shi ◽  
Lining Cai ◽  
Yukie Nakashita ◽  
...  
Keyword(s):  
Liver Injury ◽  
Physicochemical Properties ◽  
Efflux Transporter ◽  
Drug Induced ◽  
Injury Prediction ◽  
Drug Induced Liver Injury ◽  
Prediction Criteria

scholarly journals BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk

2021 ◽  
Vol 4 ◽  
Author(s):  
Yue Wu ◽  
Zhichao Liu ◽  
Leihong Wu ◽  
Minjun Chen ◽  
Weida Tong
Keyword(s):  
United States ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Liver Injury ◽  
Natural Language ◽  
Language Processing ◽  
The United States ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Drug Labeling

Background & Aims: The United States Food and Drug Administration (FDA) regulates a broad range of consumer products, which account for about 25% of the United States market. The FDA regulatory activities often involve producing and reading of a large number of documents, which is time consuming and labor intensive. To support regulatory science at FDA, we evaluated artificial intelligence (AI)-based natural language processing (NLP) of regulatory documents for text classification and compared deep learning-based models with a conventional keywords-based model.Methods: FDA drug labeling documents were used as a representative regulatory data source to classify drug-induced liver injury (DILI) risk by employing the state-of-the-art language model BERT. The resulting NLP-DILI classification model was statistically validated with both internal and external validation procedures and applied to the labeling data from the European Medicines Agency (EMA) for cross-agency application.Results: The NLP-DILI model developed using FDA labeling documents and evaluated by cross-validations in this study showed remarkable performance in DILI classification with a recall of 1 and a precision of 0.78. When cross-agency data were used to validate the model, the performance remained comparable, demonstrating that the model was portable across agencies. Results also suggested that the model was able to capture the semantic meanings of sentences in drug labeling.Conclusion: Deep learning-based NLP models performed well in DILI classification of drug labeling documents and learned the meanings of complex text in drug labeling. This proof-of-concept work demonstrated that using AI technologies to assist regulatory activities is a promising approach to modernize and advance regulatory science.

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