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

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.

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“…Thus, Pliakos et al (2019) and Fadli et al (2021) used multilabel classification based on deep learning to generate a prediction model for DTI. Multilabel classification can be used to solve binary classification problems.…”

“…To conclude which compounds and target proteins have the most potential, Erlina et al (2020) analyzed the overlapping predictions of herbal compounds across all methods. Likewise, Fadli et al (2021) have built four different models based on compound features. The four models produce several different predictive compounds, so it is necessary to perform an overlapping analysis of the predicted results.…”