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

Background The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. Methods We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. Results From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. Conclusions Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Background: The latest development of COVID-19 spread in Indonesia has reached 311,176 cases, with 11,374 patients died, updated on October 6, 2020. Unfortunately, these numbers continue to overgrow, and no drug has yet been approved for effective treatment. This study aims to determine the potential candidate compounds in Indonesian herbal medicine as a COVID-19 supportive therapy using a machine learning and pharmacophore modelling approach.Methods: For the machine learning approach, we used three classification methods that have different ways in decision making, such as Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Random Forest (RF). Moreover, for the pharmacophore modelling approach, we performed a structure-based method on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and used the SARS, MERS, and SARS-CoV-2 repurposing drugs known from the literature as data sets on the ligand-based method. Finally, we used molecular docking to analyse the interactions between the 3CLpro protein (main protease) and 14 hit compounds from the Indonesian Herbal Database (HerbalDB) and Lopinavir as a positive control.Results: The machine learning approach with SVM, RF, and MLP methods and pharmacophore modelling approach were used for screening in herbal compounds obtained from HerbalDB. Based on the screening on HerbalDB using these two prediction approaches, we got 14 hit compounds. We then performed molecular docking to determine the interaction of these compounds with the main protease SARS-CoV-2 as an inhibiting agent. From the molecular docking analysis, it was found that six potential compounds as the main proteases of the 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: We used layered virtual screening with machine learning and pharmacophore modelling approaches that could provide more objective and optimal virtual screening and avoid subjective decision making on research results. Herbal compounds from various plants have potential as antiviral candidates for SARS-CoV-2. Based on our research and literature study, one of Indonesia's potential commodity crops is Psidium guajava (guava), and people can use it directly as a preventive effort.

<p>Data tidak seimbang menjadi salah satu masalah yang muncul pada masalah prediksi atau klasifikasi. Penelitian ini memfokuskan untuk mengatasi masalah data tidak seimbang pada prediksi <em>drug-target interaction</em> (interaksi senyawa-protein). Ada banyak protein target dan senyawa obat yang terdapat pada basis data interaksi senyawa-protein yang belum divalidasi interaksinya secara eksperimen. Belum diketahuinya interaksi antar senyawa dan target tersebut membuat proporsi antara data yang diketahui interaksinya dan yang belum dikethui menjadi tidak seimbang. Data interaksi yang sangat tidak seimbang dapat menyebabkan hasil prediksi menjadi bias. Terdapat banyak cara untuk mengatasi data tidak seimbang ini, namun pada penelitian ini diimplementasikan metode yang menggabungkan <em>Biased Support Vector Machine</em> (BSVM), <em>oversampling, </em>dan <em>undersampling</em> dengan <em>Ensemble Support Vector Machine</em> (SVM). Penelitian ini mengeksplorasi efek sampling yang digabungkan dalam metode tersebut pada data interaksi senyawa-protein. Metode ini sudah diuji pada dataset <em>Nuclear Receptor,</em> <em>G-Protein Coupled Receptor</em> dan <em>Ion Channel </em>dengan rasio ketidakseimbangannya sebesar 14.6%, 32.36%, dan 28.2%. Hasil pengujian dengan menggunakan ketiga dataset tersebut menunjukkan nilai <em>area under curve</em> (AUC) secara berturut-turut sebesar 63.4%, 71.4%, 61.3% dan F-measure sebesar 54%, 60.7% dan 39%. Nilai akurasi dari metode yang digunakan masih terbilang cukup baik, walaupun nilai tersebut lebih kecil dari metode SVM tanpa perlakuan apapun. Nilai tersebut <em>bias</em> karena nilai AUC dan F-measure ternyata lebih kecil. Hal ini membuktikan bahwa metode yang diusulkan dapat menurunkan tingkat bias pada data tidak seimbang yang diuji dan meningkatkan nilai AUC dan f-measure sekitar 5%-20%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Imbalanced data </em><em>has been one of the problems that arise in processing data. This research is focusing on handling imbalanced data problem for </em><em>drug-target</em><em> </em><em>(compound-protein) interaction data. There are many target protein and drug compound existed in compound-protein interaction databases, which many interactions are not validated yet by experiment. This unknown</em><em> interaction led drug target interaction to become imbalanced data. A really imbalanced data may cause bias to prediction result. There are many ways of handling imbalanced data, but this research implemented some methods such as BSVM, oversampling, undersampling with SVM ensemble. These method already solve the imbalanced data problem on other kind of data like image data. This research is focusing on exploration of effect on the sampling that used in these method for </em><em>compound-protein</em><em> interaction data. This method had been tested on </em><em>compound-protein</em><em> interaction Nuclear Receptor, GPCR</em> <em>and Ion Channel with 14.6%, 32.36% and 28.2% of imbalance ratio. The evaluation result using these three dataset show the value of AUC respectively 63.4%, 71.4%, 61.3% and F-measure of 54%, 60.7% and 39%. The score from this method is quite good, even though the score of accuracy and precision is smaller than the SVM. The value is bias because the AUC and F-measure score is smaller. This proves that the proposed method could reduce the bias rate in the evaluated imbalanced data and increase AUC and f-measure score from 5% to 20%.</em></p><p><em><strong><br /></strong></em></p>