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

Ai, Haixin; Chen, Wen; Zhang, Li; Huang, Liangchao; Yin, Zimo; Hu, Huan; Zhao, Qi; Jian, Zhao; Liu, Huan

doi:10.1093/toxsci/kfy121

Cited by 73 publications

(100 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Predicting liver toxicity from chemical structure has been a preoccupation of over two decades, first starting somewhat timidly with local models [44,45], to move later also to global models [46,47]. Many of the previous models used relatively small size datasets (less than 400 compounds) [48,49]; studies based on datasets larger than DILIrank have also been published [11,17,[50][51][52], but the authors of DILIrank used a methodology that, in theory at least, was superior and more consistent with the totality of available clinical data. The total DILIrank dataset includes 1036 compounds, but 254 were classified as of "ambiguous DILI-concern", because the causality evidence was limited; besides, it took great care in defining DILI negatives, which varied among four large sources previously studied, labeling some compounds previously considered of no DILI concern as of less DILI concern.…”

Section: Discussionmentioning

confidence: 99%

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Ancuceanu

Hovaneț

Anghel

et al. 2020

IJMS

View full text Add to dashboard Cite

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

show abstract

Section: Discussionmentioning

confidence: 99%

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Ancuceanu

Hovaneț

Anghel

et al. 2020

IJMS

View full text Add to dashboard Cite

show abstract

“…Accurate and timely prediction of drug induced liver injury remains a challenging research topic with a great potential impact in drug R&D. A number of efforts have been made to build in silico models that can predict DILI [32][33][34]. Since the datasets and feature selection vary, a direct comparison between these methods and our approach may be inaccurate.…”

Section: Discussionmentioning

confidence: 99%

A compound attributes-based predictive model for drug induced liver injury in humans

Liu

Gao

2020

PLoS ONE

View full text Add to dashboard Cite

Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and structural properties of compounds as model input. Cross validation demonstrates that the predictive model has a robust performance with averaged 70% of both sensitivity and specificity over 500 trials. An independent validation was performed on public benchmark drugs and the results suggest potential utility of our model for identifying safety alerts. This in silico approach, upon further validation, would ultimately be implemented, together with other in vitro safety assays, for screening compounds early in drug development. OPEN ACCESSCitation: Liu Y, Gao H, He YD (2020) A compound attributes-based predictive model for drug induced liver injury in humans. PLoS ONE 15(4): e0231252.

show abstract

“…Recently, Ai et al introduced an ensemble model for model improvement to better predict DILI compounds. 22 They used 12 types of molecular fingerprints and three machine learning algorithms to create 36 corresponding classifiers and took the averaged probabilistic values returned by each classifier. According to reported findings, Ai et al’s model is the current best model to predict DILI compounds.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen-Vo

Nguyen

et al. 2020

ACS Omega

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 73 publications

References 29 publications

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

A compound attributes-based predictive model for drug induced liver injury in humans

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

Contact Info

Product

Resources

About