Evidence map›Paper›PMID 35922786›Full record

ArticleBMC complementary medicine and therapies2022

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches.

Linda Erlina, Rafika Indah Paramita, Wisnu Ananta Kusuma, Fadilah Fadilah, Aryo Tedjo, Irandi Putra Pratomo, Nabila Sekar Ramadhanti, Ahmad Kamal Nasution, Fadhlal Khaliq Surado, Aries Fitriawan and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMC complementary medicine and therapies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 32 citations in OpenAlex.

  1. Review
  2. Strategies in Taxol Development: A Review.Mini reviews in medicinal chemistry · 2025
    Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Pan-Genomics ofLife (Basel, Switzerland) · 2023
    Article
  9. Application of artificial intelligence in the development ofFrontiers in artificial intelligence · 2023
    Article
  10. Article
  11. Review
  12. Review
  13. Review
  14. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Linda Erlina *Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 4, Jakarta, 10430, Indonesia.
Rafika Indah Paramita *Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 4, Jakarta, 10430, Indonesia. rafikaindah@ui.ac.id.
Wisnu Ananta Kusuma *Department of Computer Science, Faculty of Mathematics and Natural Science, IPB University, Jalan Meranti Wing 20 level 5 Kampus IPB, Bogor, West Java, 16680, Indonesia. ananta@apps.ipb.ac.id.ORCID http://orcid.org/0000-0002-3682-244X
Fadilah FadilahDepartment of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 4, Jakarta, 10430, Indonesia.
Aryo TedjoDepartment of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 4, Jakarta, 10430, Indonesia.
Irandi Putra PratomoBioinformatics Core Facilities - IMERI, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 6, Jakarta, 10430, Indonesia.
Nabila Sekar RamadhantiDepartment of Computer Science, Faculty of Mathematics and Natural Science, IPB University, Jalan Meranti Wing 20 level 5 Kampus IPB, Bogor, West Java, 16680, Indonesia.
Ahmad Kamal NasutionDepartment of Computer Science, Faculty of Mathematics and Natural Science, IPB University, Jalan Meranti Wing 20 level 5 Kampus IPB, Bogor, West Java, 16680, Indonesia.
Fadhlal Khaliq SuradoDepartment of Computer Science, Faculty of Mathematics and Natural Science, IPB University, Jalan Meranti Wing 20 level 5 Kampus IPB, Bogor, West Java, 16680, Indonesia.
Aries FitriawanDepartment of Computer Science, Faculty of Mathematics and Natural Science, IPB University, Jalan Meranti Wing 20 level 5 Kampus IPB, Bogor, West Java, 16680, Indonesia.
Khaerunissa Anbar IstiadiBioinformatics Core Facilities - IMERI, Faculty of Medicine, Universitas Indonesia, Jalan Salemba Raya number 6, Jakarta, 10430, Indonesia.
Arry YanuarBiomedical Computational and Drug Design Laboratory, Faculty of Pharmacy, Universitas Indonesia, Kampus Baru UI, Depok, West Java, 16424, Indonesia.
University of Indonesia · IDIPB University · ID

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsWe 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.

resultsFrom 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.

conclusionsOur 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.

Indexed as

COVID-19 Drug TreatmentHesperidinMethyl EthersGlucosidesHumansIndonesiaKaempferolsMachine LearningMolecular Docking SimulationQuercetinSARS-CoV-2GlucosidesHesperidinKaempferolsMethyl EthersQuercetin3CLProCOVID-19Indonesian Herbal CompoundsMachine LearningMolecular DockingPharmacophore ModelingSARS-CoV-2

Identifiers

PMID35922786
PMCPMC9347098
OpenAlexW4289544340

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.