@article{Yuda_2022, title={LIGAND-BASED VIRTUAL SCREENING ON NATURAL COMPOUND PRODUCTS AGAINST SARS-COV-2 MAIN PROTEASE (Mpro) USING SUBSTRUCTURE SIMILARITY AND MACHINE LEARNING APPROACH}, volume={9}, url={https://bimfi.e-journal.id/bimfi/article/view/86}, DOI={10.48177/bimfi.v9i1.86}, abstractNote={Introduction: Increased ROS (reactive oxygen species) and SASP (senescence-associated secretory phenotype) lead to skin aging via cellular senescence. CD36 (Cluster Difference 36) is found overexpressed in senescent cells and accepts various activators that generate ROS and SASP. Cinnamon (Cinnamomum zeylanicum) has been known to exert several pharmacological effects like antioxidant and anti-senescence. However, its anti-senescence effect on CD36 has not been reported yet. This study aims to prove that cinnamon’s compounds are effective to inhibit CD36 in order to stop skin aging caused by senescence. Methods: Literature studies and in silico approaches such as database searching, molecular docking, and KNIME open analytic platform were used in this study. Result: Cinnamaldehyde is proven as a better competitive CD36 inhibitor (DICE Score: 0,886; Tanimoto Score: 0,939) with better affinity than native ligand and previously studied inhibitors (RMSD: 0,74 Å; S: -7,43 kcal/mol). Bioinformatics investigations also showed that major compounds of cinnamon target CD36 regulator, oxidoreductases, and SASP-producing receptors that co-expressed with CD36. Conclusion: Active components of cinnamon are potential to be an anti skin aging by inhibiting CD36, regulating CD36, and eradicating senescent factors.}, number={1}, journal={Berkala Ilmiah Mahasiswa Farmasi Indonesia}, author={Yuda, GP. Wahyunanda Crista}, year={2022}, month={Jun.}, pages={25–41} }

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    <timestamp>20241204084628000</timestamp>
    <depositor>
      <depositor_name>Syafura Az-Zahra</depositor_name>
      <email_address>bimfi.ismafarsi@gmail.com</email_address>
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    <registrant>Ikatan Senat Mahasiswa Farmasi Seluruh Indonesia</registrant>
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    <journal>
      <journal_metadata>
        <full_title>Berkala Ilmiah Mahasiswa Farmasi Indonesia</full_title>
        <abbrev_title>BIMFI</abbrev_title>
        <issn media_type="electronic">2774-1710</issn>
        <issn media_type="print">2302-7851</issn>
      </journal_metadata>
      <journal_issue>
        <publication_date media_type="online">
          <month>06</month>
          <day>30</day>
          <year>2024</year>
        </publication_date>
        <journal_volume>
          <volume>11</volume>
        </journal_volume>
        <issue>1</issue>
      </journal_issue>
      <journal_article xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" publication_type="full_text" metadata_distribution_opts="any" language="id">
        <titles>
          <title>ANALISIS BIBLIOMETRIK TERHADAP PENGEMBANGAN PENELITIAN MACHINE LEARNING UNTUK IDENTIFIKASI SENYAWA KIMIA OBAT</title>
        </titles>
        <titles>
          <title>BIBLIOMETRIC ANALYSIS OF RESEARCH PROGRESS ON MACHINE LEARNING FOR IDENTIFICATION OF DRUG CHEMICAL COMPOUNDS</title>
        </titles>
        <contributors>
          <person_name contributor_role="author" sequence="first" language="id">
            <given_name>Tambunan Matthew</given_name>
            <surname>Valentino</surname>
          </person_name>
          <person_name contributor_role="author" sequence="additional" language="id">
            <given_name>I Made Agus Kusuma</given_name>
            <surname>Adi</surname>
          </person_name>
          <person_name contributor_role="author" sequence="additional" language="id">
            <given_name>Bellyna Putri Annisa</given_name>
            <surname>Rahmadhani</surname>
          </person_name>
          <person_name contributor_role="author" sequence="additional" language="id">
            <given_name>Bellyna Putri Annisa</given_name>
            <surname>Rahmadhani</surname>
          </person_name>
        </contributors>
        <jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xml:lang="id">
          <jats:p>Pendahuluan: Perkembangan teknologi berbasis machine learning telah dimanfaatkan dalam berbagai bidang ilmu mulai dari pendidikan hingga pekerjaan sosial. Kesehatan menjadi salah satu yang memanfaatkan penerapan teknologi machine learning. Hal ini dapat dibuktikan dengan berkembangnya teknologi yang membantu dalam analisis pengobatan serta senyawa kimia obat. Analisis bibliometrik bertujuan memberikan visualisasi metadata pustaka terkait terhadap topik machine learning untuk identifikasi senyawa kimia obat.&#13;
Metode: Metode yang digunakan yakni dengan pengumpulan pustaka menggunakan database Pubmed dengan kata kunci “(machine learning) AND (drug structure)”. Faktor eksklusi yang diterapkan untuk pencarian adalah tipe dokumen, akses dokumen, dan tahun publikasi. Pustaka yang diperoleh dianalisis secara bibliometrik menggunakan Biblioshiny oleh Bibliometrix. Analisis bibliometrik dilakukan terhadap jumlah publikasi tiap tahun, jurnal ilmiah paling relevan, penulis paling relevan, afiliasi paling relevan, negara penerbit ilmiah, jaringan kesertaan, dan jaringan kolaborasi antar penulis.&#13;
Hasil: Analisis bibliometrik banyak dikembangkan oleh peneliti hal ini, dilihat dari banyaknya artikel yang dipublikasikan oleh peneliti. Analisis ini mengidentifikasi penelitian sebanyak 8.301 penulis dari 79 negara. Jumlah artikel yang relevan terhadap topik paling banyak terdapat pada jurnal Scientific Report.&#13;
Kesimpulan: Pemanfaatan machine learning mengalami perkembangan yang pesat dalam 10 tahun terakhir. Analisis bibliometrik dengan Biblioshiny memberikan visualisasi yang tepat mengenai perkembangan tersebut. Analisis bibliometrik berhasil mengidentifikasi tren penelitian oleh 8.301 penulis dari 79 negara pada rentang tahun 2014-2024. Jumlah publikasi tiap tahun tertinggi terjadi pada tahun 2023 dan kontinuitas peneliti dimulai dari tahun 2020. Kata kunci dan kolaborasi dianalisis melalui node dan edge pada skema Louvain.&#13;
Kata kunci: Bibliografi, Pemelajaran Mesin, Desain Obat</jats:p>
        </jats:abstract>
        <jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xml:lang="en">
          <jats:p>Introduction: The development of machine learning-based technology has been utilized in various fields of science ranging from education to social work. The field of health science is one that utilizes the application of machine learning technology, this can be proven by the development of technology that helps in the analysis of treatment and chemical compounds of drugs. The bibliometric analysis aims to provide a visualization of related literature metadata on the topic of machine learning for the analysis of chemical compounds of drugs.&#13;
Methods: The method used was literature collection using the Pubmed database with the keywords "(machine learning) AND (drug structure)". Exclusion factors applied for the search were document type, document access, and publication year. The literature obtained was analyzed bibliometrically using Biblioshiny by Bibliometrix. Bibliometric analysis was analyzed in three clusters.&#13;
Result: Bibliometric analysis is widely developed by researchers, it can be seen from the number of articles published by researchers. This analysis identified research by 8,301 authors from 79 countries. The largest number of articles relevant to the topic is found in the Scientific Report journal.&#13;
Conclusion: The utilization of machine learning has grown rapidly in the last 10 years. Bibliometric analysis with Biblioshiny provides a precise visualization of these developments. The bibliometric analysis identified research trends by 8,301 authors from 79 countries between 2014 and 2024. The highest number of publications per year occurred in 2023 and researcher continuity started from 2020. Keywords and collaboration analysed using node and edge from Louvain scheme. </jats:p>
        </jats:abstract>
        <publication_date media_type="online">
          <month>06</month>
          <day>30</day>
          <year>2024</year>
        </publication_date>
        <pages>
          <first_page>8</first_page>
          <last_page>16</last_page>
        </pages>
        <doi_data>
          <doi>10.48177/bimfi.v11i1.121</doi>
          <resource>https://bimfi.e-journal.id/bimfi/article/view/121</resource>
          <collection property="crawler-based"/>
          <collection property="text-mining"/>
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