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Background Status of the latest developments from the spread of COVID-19 in Indonesia has reached 15438 cases with 1028 cases of patients died, updated on May 13, 2020. Unfortunately, the number of infected continues to overgrow, and no drugs have been approved for effective treatment. This research aims to find potential candidate compounds in Indonesian herbal as COVID-19 supportive therapy using machine learning and pharmacophore modeling approach. Methods For a machine learning approach, we used three classification methods that have different principles in decision making, such as SVM, MLP, and Random Forest. By using these different methods, it is expected that more optimal screening results can be obtained than using only one method. Moreover, for a pharmacophore modeling approach, we did the structure-based method on the 3D structure of SARS-CoV-2 main protease (3CLPro) and using known SARS, MERS, and SARS-CoV-2 repurposing drugs from literature as data sets on the ligand-based method. Lastly, we used molecular docking to analyse the interaction between 3CLpro (main protease) protein with 14 hit compounds from the Indonesian Herbal Database (HerbalDB) and Lopinavir as a positive control. Results The models yielded by SVM, RF, and MLP were used for screening in herbal compounds obtained from HerbalDB and got 125 potential compounds. Whereas the structure-based pharmacophore modeling gave eight hit compounds and the ligand-based methods produced more than a hundred hit compounds. Based on the screening on HerbalDB using these two prediction approaches, we got 14 hit compounds candidates. Further analysis was done using molecular docking to know the interaction between each compound and main protease of SARS-CoV-2 as inhibitory agents. From molecular docking analysis, we got six potential compounds as the main protease of SARS-CoV-2 inhibitor, i.e Hesperidin, Kaempferol-3,4'-di-O-methyl ether (Ermanin); Myricetin-3-glucoside, Peonidine 3-(4’-arabinosylglucoside); Quercetin 3-(2G-rhamnosylrutinoside); and Rhamnetin 3-mannosyl-(1–2)-alloside. Conclusions Herbal compounds from various plants were potential as candidates of SARS-CoV-2 antivirals. Based on our research and literature study, one of the potential commodity crops in Indonesia is Psidium guajava (guava) and can be directly used by the community.

Coronavirus disease 2019 (COVID-19) is an infectious disease of the respiratory system that caused a pandemic in 2020. There is still not any effective special treatment to cure it. Drug repositioning is used to find an effective drug for curing new diseases by finding new efficacy of registered drug. The new efficacy can be conducted by elaborating the interactions between compounds and proteins (DTI). Deep Semi-Supervised Learning (DSSL) is used to overcome the lack of DTI information. DSSL utilizes unsupervised learning algorithms such as Stacked Auto Encoder (SAE) as pre-training for initializing weights on the Deep Neural Network (DNN). This study uses DSSL with a feature-based chemogenomics approach on the data resulted from the exploration of potential anti-coronavirus treatment. This study finds that the use of fingerprints for compound features and Dipeptide Composition (DC) for protein features gives the best results on accuracy (0.94), recall (0.83), precision (0.817), F-measure (0.822), and AUROC (0.97). From the test data predictions, 1766 and 929 positive interactions are found on the test data and herbal compounds, respectively. Keywords-coronavirus disease 2019, drug repositioning, deep semi-supervised learning, stacked autoencoder, deep neural network II. DEEP SEMI-SUPERVISED LEARNING FOR DTIResearch on drug repositioning is based on the fact that most drug compounds can activate or inhibit the biological functions of the target protein. This creates the needs to develop a DTI identification system [11]. DTI identification

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Andrographis paniculata is known as the king of bitter and it has been widely used as a medicinal plant. The properties of A. paniculata are generally determined by the metabolite composition, which may be influenced by several factors, one of which is the part of the plant extracted. The objectives of this research are to identify putatively the metabolite composition of the stem and the leaves extracts using LC-MS/MS and classify them using PCA. The stem and the leaves samples were separated and extracted using sonication with 70% ethanol. A total of 31 metabolite compounds has been putatively identified. All compounds were identified in the stem and the leaves extracts, which only differed in their intensity. These metabolite compounds were divided into diterpene lactones, flavonoids, and phenolic acid groups. By using the peak intensities of the 18 compounds identified, the leaves and stem extracts were grouped using PCA.