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Article

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

by
Aulia Fadli
1,
Wisnu Ananta Kusuma
1,2,*,
Annisa
1,
Irmanida Batubara
2,3 and
Rudi Heryanto
2,3
1
Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor 16680, Indonesia
2
Tropical Biopharmaca Research Center, IPB University, Bogor 16680, Indonesia
3
Department of Chemistry, Faculty of Mathematics and Natural Sciences, IPB University, Bogor 16680, Indonesia
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2021, 5(4), 75; https://doi.org/10.3390/bdcc5040075
Submission received: 7 November 2021 / Revised: 27 November 2021 / Accepted: 6 December 2021 / Published: 9 December 2021
(This article belongs to the Topic Machine and Deep Learning)
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Abstract

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.
Keywords: coronavirus disease 2019; drug repurposing; drug-target interaction; health; multilabel classification; stack autoencoder-deep neural network coronavirus disease 2019; drug repurposing; drug-target interaction; health; multilabel classification; stack autoencoder-deep neural network

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MDPI and ACS Style

Fadli, A.; Kusuma, W.A.; Annisa; Batubara, I.; Heryanto, R. Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease. Big Data Cogn. Comput. 2021, 5, 75. https://doi.org/10.3390/bdcc5040075

AMA Style

Fadli A, Kusuma WA, Annisa, Batubara I, Heryanto R. Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease. Big Data and Cognitive Computing. 2021; 5(4):75. https://doi.org/10.3390/bdcc5040075

Chicago/Turabian Style

Fadli, Aulia, Wisnu Ananta Kusuma, Annisa, Irmanida Batubara, and Rudi Heryanto. 2021. "Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease" Big Data and Cognitive Computing 5, no. 4: 75. https://doi.org/10.3390/bdcc5040075

APA Style

Fadli, A., Kusuma, W. A., Annisa, Batubara, I., & Heryanto, R. (2021). Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease. Big Data and Cognitive Computing, 5(4), 75. https://doi.org/10.3390/bdcc5040075

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