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 If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets.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 Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports

2021 ◽  
Vol 12 ◽  
Author(s):  
Wojciech Lesiński ◽  
Krzysztof Mnich ◽  
Witold R. Rudnicki
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Liver Injury ◽  
Cell Lines ◽  
Predictive Models ◽  
Molecular Descriptors ◽  
Chemical Properties ◽  
Human Cell Lines ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predictive models of risk of DILI for chemical compounds using multiple sources of information.Methods: Using several supervised machine learning algorithms, we built predictive models for several alternative splits of compounds between DILI and non-DILI classes. To this end, we used chemical properties of the given compounds, their effects on gene expression levels in six human cell lines treated with them, as well as their toxicological profiles. First, we identified the most informative variables in all data sets. Then, these variables were used to build machine learning models. Finally, composite models were built with the Super Learner approach. All modeling was performed using multiple repeats of cross-validation for unbiased and precise estimates of performance.Results: With one exception, gene expression profiles of human cell lines were non-informative and resulted in random models. Toxicological reports were not useful for prediction of DILI. The best results were obtained for models discerning between harmless compounds and those for which any level of DILI was observed (AUC = 0.75). These models were built with Random Forest algorithm that used molecular descriptors.

scholarly journals Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features

2021 ◽  
Author(s):  
TH Nguyen-Vo ◽  
L Nguyen ◽  
N Do ◽  
PH Le ◽  
TN Nguyen ◽  
...  
Keyword(s):  
Liver Injury ◽  
Language Processing ◽  
American Chemical Society ◽  
In Vivo Studies ◽  
Drug Induced ◽  
Molecular Fingerprint ◽  
Molecular Fingerprints ◽  
Safety Testing ◽  
Drug Induced Liver Injury

© 2020 American Chemical Society. As a critical issue in drug development and postmarketing safety surveillance, drug-induced liver injury (DILI) leads to failures in clinical trials as well as retractions of on-market approved drugs. Therefore, it is important to identify DILI compounds in the early-stages through in silico and in vivo studies. It is difficult using conventional safety testing methods, since the predictive power of most of the existing frameworks is insufficiently effective to address this pharmacological issue. In our study, we employ a natural language processing (NLP) inspired computational framework using convolutional neural networks and molecular fingerprint-embedded features. Our development set and independent test set have 1597 and 322 compounds, respectively. These samples were collected from previous studies and matched with established chemical databases for structural validity. Our study comes up with an average accuracy of 0.89, Matthews's correlation coefficient (MCC) of 0.80, and an AUC of 0.96. Our results show a significant improvement in the AUC values compared to the recent best model with a boost of 6.67%, from 0.90 to 0.96. Also, based on our findings, molecular fingerprint-embedded featurizer is an effective molecular representation for future biological and biochemical studies besides the application of classic molecular fingerprints.

scholarly journals Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features

2021 ◽  
Author(s):  
TH Nguyen-Vo ◽  
L Nguyen ◽  
N Do ◽  
PH Le ◽  
TN Nguyen ◽  
...  
Keyword(s):  
Liver Injury ◽  
Language Processing ◽  
American Chemical Society ◽  
In Vivo Studies ◽  
Drug Induced ◽  
Molecular Fingerprint ◽  
Molecular Fingerprints ◽  
Safety Testing ◽  
Drug Induced Liver Injury

© 2020 American Chemical Society. As a critical issue in drug development and postmarketing safety surveillance, drug-induced liver injury (DILI) leads to failures in clinical trials as well as retractions of on-market approved drugs. Therefore, it is important to identify DILI compounds in the early-stages through in silico and in vivo studies. It is difficult using conventional safety testing methods, since the predictive power of most of the existing frameworks is insufficiently effective to address this pharmacological issue. In our study, we employ a natural language processing (NLP) inspired computational framework using convolutional neural networks and molecular fingerprint-embedded features. Our development set and independent test set have 1597 and 322 compounds, respectively. These samples were collected from previous studies and matched with established chemical databases for structural validity. Our study comes up with an average accuracy of 0.89, Matthews's correlation coefficient (MCC) of 0.80, and an AUC of 0.96. Our results show a significant improvement in the AUC values compared to the recent best model with a boost of 6.67%, from 0.90 to 0.96. Also, based on our findings, molecular fingerprint-embedded featurizer is an effective molecular representation for future biological and biochemical studies besides the application of classic molecular fingerprints.

scholarly journals Predicting Drug-Induced Liver Injury Using Ensemble Learning Methods and Molecular Fingerprints

2018 ◽  
Vol 165 (1) ◽  
pp. 100-107 ◽  
Author(s):  
Haixin Ai ◽  
Wen Chen ◽  
Li Zhang ◽  
Liangchao Huang ◽  
Zimo Yin ◽  
...  
Keyword(s):  
Liver Injury ◽  
Ensemble Learning ◽  
Learning Methods ◽  
Drug Induced ◽  
Molecular Fingerprints ◽  
Drug Induced Liver Injury
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