Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an efficient and economical approach. Research in drug...

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Bibliographic Details
Published inBig data and cognitive computing Vol. 5; no. 4; p. 75
Main Authors Fadli, Aulia, Kusuma, Wisnu Ananta, Annisa, Batubara, Irmanida, Heryanto, Rudi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2021
Subjects
Accuracy
Algorithms
Artificial neural networks
Classification
coronavirus disease 2019
Coronaviruses
COVID-19
Datasets
Daylight
drug repurposing
drug-target interaction
Drugs
Feature extraction
Fingerprints
health
Infectious diseases
Kinases
Machine learning
multilabel classification
Neural networks
Pandemics
Predictions
Proteins
R&D
Research & development
Severe acute respiratory syndrome coronavirus 2
stack autoencoder-deep neural network
Viral diseases
Indonesia
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Summary:Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an efficient and economical approach. Research in drug repurposing can be done by observing the interactions of drug compounds with protein related to a disease (DTI), then predicting the new drug-target interactions. This study conducted multilabel DTI prediction using the stack autoencoder-deep neural network (SAE-DNN) algorithm. Compound features were extracted using PubChem fingerprint, daylight fingerprint, MACCS fingerprint, and circular fingerprint. The results showed that the SAE-DNN model was able to predict DTI in COVID-19 cases with good performance. The SAE-DNN model with a circular fingerprint dataset produced the best average metrics with an accuracy of 0.831, recall of 0.918, precision of 0.888, and F-measure of 0.89. Herbal compounds prediction results using the SAE-DNN model with the circular, daylight, and PubChem fingerprint dataset resulted in 92, 65, and 79 herbal compounds contained in herbal plants in Indonesia respectively.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc5040075