The development and application of <i>in silico</i> models for drug induced liver injury

Li, Xiao; Chen, Yaojie; Song, Xinrui; Zhang, Yuan; Li, Huanhuan; Zhao, Yong

doi:10.1039/c7ra12957b

Cited by 34 publications

(37 citation statements)

References 59 publications

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“…On Table 4 Average model performance and standard deviation (SD) for the best performing DNN models at each target for the validation data sets when adjusted activity thresholds of 0.1 and 0.9 were applied. Full results can be found in the ESI (Tables S11 and S12) achieve accuracy values above 90% in binary classication tasks, [1][2][3][4][5] although the difficulty of the task must be considered when comparing model performance. The models also do not show excessive levels of overtting, as can be a problem with DNNs.…”

Section: Resultsmentioning

confidence: 99%

“…A wide variety of diverse and important human targets were chosen for DNN classier construction, including the well-known Bowes targets 37 and an extended list identied in our previous work 38 including targets published by Sipes et al 39 Many of the previously published papers on machine learning only provide models for a single biological target or endpoint. [1][2][3][4][5]11,12,15 This limits their usefulness and coverage of human toxicology, which we aim to overcome by constructing models for a large number of MIEs. Generating models for this extended list of targets provides a wider screen of potential molecular toxicity.…”

Section: Introductionmentioning

confidence: 99%

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Neural network activation similarity: a new measure to assist decision making in chemical toxicology

et al. 2020

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural network activation similarity: a new measure to assist decision making in chemical toxicology

et al. 2020

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“…Machine learning models can be used to predict hepatotoxicity given the molecular descriptor of a compound as input. We mainly focused on the following machine learning algorithms to develop binary classification models, among several methods that have been applied in QSAR modeling: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Logistic Regression (LR), Random Forest (RF), XG Boosting (XGB), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Decision Tree (DT) classifier [ 27 , 32 , 33 , 53 , 55 , 56 ]. These robust algorithms are highly efficient and can accommodate numerous features.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets

Jaganathan

Tayara

Chong

2021

IJMS

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Drug-induced liver toxicity is one of the significant safety challenges for the patient’s health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. Thus, it is essential to identify hepatotoxic compounds in the initial stages of drug development process. The purpose of this study is to construct quantitative structure activity relationship models using machine learning algorithms and systematical feature selection methods for molecular descriptor sets. The models were built from a large and diverse set of 1253 drug compounds and were validated internally with 10-fold cross-validation. In this study, we applied a variety of feature selection techniques to extract the optimal subset of descriptors as modeling features to improve the prediction performance. Experimental results suggested that the support vector machine-based classifier had achieved a better classification accuracy with reduced molecular descriptors. The final optimal model provides an accuracy of 0.811, a sensitivity of 0.840, a specificity of 0.783 and Mathew’s correlation coefficient of 0.623 with an internal validation set. Furthermore, this model outperformed the prior studies while evaluated in both the internal and external test sets. The utilization of distinct optimal molecular descriptors as modeling features produce an in silico model with a superior performance.

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“…Besides, SA also can be used in other fields such as cosmetic research and environmental protection. Several groups of SAs have been reported for different toxic endpoints (Amberg et al, ; Benigni & Bossa, ; Li et al, ; Li et al, , ; Li et al, ; Mellor, Steinmetz, & Cronin, ).…”

Section: Methodsmentioning

confidence: 99%

In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts

Cui

Liu

Zhang

et al. 2019

J of Applied Toxicology

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Drug-induced rhabdomyolysis (DIR) is a serious adverse reaction and can be fatal. In the present study, we focused on the modeling and understanding of the molecular basis of DIR of small molecule drugs. A series of machine-learning models were developed using an Online Chemical Modeling Environment platform with a diverse dataset. A total of 80 machine-learning models were generated. Based on the topperforming individual models, a consensus model was also developed. The consensus model was available at https://ochem.eu/model/32214665, and the individual models can be accessed with the corresponding model IDs on the website. Furthermore, we also analyzed the difference of distributions of eight key physicochemical properties between rhabdomyolysis-inducing drugs and non-rhabdomyolysisinducing drugs. Finally, structural alerts responsible for DIR were identified from fragments of the Klekota-Roth fingerprints.

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The development and application of in silico models for drug induced liver injury

Abstract: Drug-induced liver injury (DILI), caused by drugs, herbal agents or nutritional supplements, is a major issue for patients and the pharmaceutical industry.

Cited by 34 publications

References 59 publications

Neural network activation similarity: a new measure to assist decision making in chemical toxicology

Neural network activation similarity: a new measure to assist decision making in chemical toxicology

Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets

In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts

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