Deep Learning for Drug-Induced Liver Injury

Xu, Youjun; Dai, Ziwei; Chen, Fangjin; Gao, Shuaishi; Pei, Jianfeng; Lai, Luhua

doi:10.1021/acs.jcim.5b00238

Cited by 299 publications

(265 citation statements)

References 52 publications

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“…80 In the future, we plan to integrate more relevant biological descriptors and tissue-specific expression profiles of drug targets to further improve the model performance. Furthermore, replacement of the currently used NN algorithm by deep learning algorithms 81,82 could further improve accuracy. Finally, the predicted CV complications by the combined classifiers should be further validated by experimental assays or pharmacoepidemiologic analyses from the real-world data (e.g., electronic medical records or health insurance claims databases) 83 in the future.…”

Section: Discussionmentioning

confidence: 99%

In SilicoPharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers

Cai

Fang

Guo

et al. 2018

J. Chem. Inf. Model.

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Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on use of multitudinous post-marketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and post-marketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery, but throughout the life of a drug including clinical trials and post-marketing surveillance.

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Section: Discussionmentioning

confidence: 99%

In SilicoPharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers

Cai

Fang

Guo

et al. 2018

J. Chem. Inf. Model.

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“…14 DL approaches were successfully implemented to predict drug-target interactions 15 , model reaction properties of molecules 16 and calculate toxicity of drugs. 17 As deep networks incorporate more features from biology 18 , application breadth and accuracy will likely increase.…”

Section: Inroductionmentioning

confidence: 99%

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

et al. 2016

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Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF‐7 and PC‐3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem, however, models based on a pathway level classification perform better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

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“…With atoms as nodes and bonds as edges, each node is sequentially traversed [68,84,85]. This would permit an understanding of the relationship between structure and reactivity [86].…”

Section: Chemical Structure Descriptorsmentioning

confidence: 99%

“…This would permit an understanding of the relationship between structure and reactivity [86]. Being sensitive to time sequence or succession, RNN and its variant long short-term memory (LSTM) are used to construct this kind of molecular fragments [84,85]. …”

Section: Chemical Structure Descriptorsmentioning

confidence: 99%

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Wang

2018

IJMS

149

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Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug’s preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.

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Deep Learning for Drug-Induced Liver Injury

Cited by 299 publications

References 52 publications

In SilicoPharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers

In SilicoPharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

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