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

journal contribution
posted on 26.03.2021, 10:11 by TH Nguyen-Vo, L Nguyen, N Do, PH Le, TN Nguyen, Binh Nguyen, L Le
© 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.

History

Preferred citation

Nguyen-Vo, T. H., Nguyen, L., Do, N., Le, P. H., Nguyen, T. N., Nguyen, B. P. & Le, L. (2020). Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features. ACS Omega, 5(39), 25432-25439. https://doi.org/10.1021/acsomega.0c03866

Journal title

ACS Omega

Volume

5

Issue

39

Publication date

06/10/2020

Pagination

25432-25439

Publisher

American Chemical Society (ACS)

Publication status

Published

Online publication date

22/09/2020

ISSN

2470-1343

eISSN

2470-1343

Language

en