Review Artikel: Pendekatan in Silico dalam Kimia Medisinal tentang Resistensi Antibiotik

https://doi.org/10.35912/jimi.v4i1.4561
Published: Sep 25, 2024

Abstract:

Purpose: This study aimed to explore the utilization of in silico approaches in medicinal chemistry to address the global challenge of antibiotic resistance through faster, more efficient, and innovative drug discovery methods..

Methodology: A systematic literature review was conducted using the Google Scholar, SINTA, PubMed, and ScienceDirect databases. The selected articles highlight computational techniques such as molecular docking, machine learning, and Perturbation Theory and Machine Learning (PTML) models for the identification, screening, and optimization of novel antibiotic candidates.

Results: This review reveals that in silico methods significantly accelerate the discovery of potential antibiotics, facilitate the identification of compounds with novel mechanisms of action, improve ligand–target binding predictions, and support the modulation of resistance-related gene expression. The synergy between computational simulations and experimental validation enhances the reliability and efficiency of drug development pipelines.

Conclusions In silico approaches offer effective, rapid, and cost-efficient solutions for the discovery of next-generation antibiotics that play a crucial role in the ongoing battle against antimicrobial resistance.

Limitations: Despite their advantages, in silico methods have limitations in accurately predicting pharmacokinetics and toxicity profiles, as well as in modelling protein flexibility and complexity of biological systems.

Contribution: This study contributes to medicinal chemistry, pharmaceutical science, and biotechnology by providing an integrative framework that supports innovative and resource-efficient strategies for antibiotic development and resistance mitigation.

Keywords:
1. Antibiotics
2. In Silico
3. Machine Learning
4. Medicinal Chemistry
5. Molecular Docking
Authors:
1 . Saeful Amin
2 . Syifa Mustafidah
3 . Nazelia Saila Nabila
4 . Cici Maharani
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How to Cite
Amin, S. ., Mustafidah, S., Nabila, N. S. ., & Maharani, C. (2024). Review Artikel: Pendekatan in Silico dalam Kimia Medisinal tentang Resistensi Antibiotik. Jurnal Ilmu Medis Indonesia, 4(1), 83–91. https://doi.org/10.35912/jimi.v4i1.4561
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Issue & Section
Vol. 4 No. 1 (2024): September
Articles
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References

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    Amin, S., Andini, M., Alfarizi, A., & Pratomo, D. W. (2025). Eksplorasi Molekuler Docking Senyawa Antivirus terhadap Target Protein SARS-CoV-2?: Peran Umifenovir sebagai Kandidat Terapeutik dalam Kimia Medisinal COVID-19. 5(3), 17–26.

    Amin, S., & Nabila, L. S. (n.d.). Review artikel?: Peran Pendekatan In Silico Dalam Kimia Medisinal. 1(6), 1345–1349.

    Amin, S., Azijah, R. N., & Gunawan, F. R. (2024). Eksplorasi Senyawa Alami sebagai Lead Antikanker Payudara dengan Pendekatan In Silico. Jurnal Ilmu Medis Indonesia, 4(1), 63-74. doi:10.35912/jimi.v4i1.4560

    Amin, S., Hurry, Z. A. Z., Sumantri, T. A., & Fauzi, R. A. (2024). Studi Komputasional Senyawa Flavonoid Tanaman Obat sebagai Kandidat Agen Antidiabetik. Jurnal Ilmu Medis Indonesia, 4(1), 21-40. doi:10.35912/jimi.v4i1.4553

    Amin, S., Pujiyani, D., Rusiyana, N. P., & Azzahra, S. M. (2024). Evaluasi Potensi Antikanker Senyawa Daun Kelor melalui Kimia Medisinal. Jurnal Ilmu Medis Indonesia, 4(1), 75-82. doi:10.35912/jimi.v4i1.4544

    Amin, S., Supriatna, G. T., Ardian, M. I., & Abdurrahman, M. I. (2024). Potensi Senyawa Turunan Terpenoid sebagai Agen Anti-Kanker. Jurnal Ilmu Medis Indonesia, 4(1), 53-61. doi:10.35912/jimi.v4i1.4551

    Amin, S., Wihdatunnisa, I., Aisyah, R., & Kurniawan, Y. S. (2024). Potensi Senyawa Kuersetin sebagai Antikanker Payudara melalui Pendekatan Molecular Docking. Jurnal Ilmu Medis Indonesia, 4(1), 41-51. doi:10.35912/jimi.v4i1.4565

    Amon, D., Manu, P., Asante-Kwatia, E., Mante, P. K., Danquah, C. A., Borquaye, L. S., & Ekuadzi, E. (2024). Antimicrobial resistance modifying effects and molecular docking studies of Affinine, derived from Tabernaemontana crassa. Scientific African, 26, e02382. doi:https://doi.org/10.1016/j.sciaf.2024.e02382

    Cardona, S. T., Rahman, A. Z., & Novomisky Nechcoff, J. (2025). Innovative perspectives on the discovery of small molecule antibiotics. npj Antimicrobials and Resistance, 3(1), 19. doi:https://doi.org/10.1038/s44259-025-00089-0

    Ekins, S., Mestres, J., & Testa, B. (2007). In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. British Journal of Pharmacology, 152(1), 9–20. https://doi.org/10.1038/sj.bjp.0707305

    Jiménez-Osés, G., Osuna, S., Gao, X., Sawaya, M. R., Gilson, L., Collier, S. J., . . . Houk, K. (2014). The role of distant mutations and allosteric regulation on LovD active site dynamics. Nature chemical biology, 10(6), 431-436. doi:https://doi.org/10.1038/nchembio.1503

    Juki?, M., & Bren, U. (2022). Machine Learning in Antibacterial Drug Design. Frontiers in Pharmacology, 13(May), 1–11. https://doi.org/10.3389/fphar.2022.864412

    Kakoty, V., Kalarikkal Chandran, S., Gulati, M., Goh, B. H., Dua, K., & Kumar Singh, S. (2023). Senolytics: Opening avenues in drug discovery to find novel therapeutics for Parkinson’s Disease. Drug Discovery Today, 28(6), 103582. https://doi.org/10.1016/J.DRUDIS.2023.103582

    Kleandrova, V. V., Cordeiro, M. N. D. S., & Speck-Planche, A. (2025). In Silico Approach for Antibacterial Discovery: PTML Modeling of Virtual Multi-Strain Inhibitors Against Staphylococcus aureus. Pharmaceuticals, 18(2), 1–23. https://doi.org/10.3390/ph18020196

    Lionta, E., Spyrou, G., Vassilatis, D., & Cournia, Z. (2014). Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances. Current Topics in Medicinal Chemistry, 14(16), 1923–1938. https://doi.org/10.2174/1568026614666140929124445

    Plé, C., Tam, H. K., Vieira Da Cruz, A., Compagne, N., Jiménez-Castellanos, J. C., Müller, R. T., Pradel, E., Foong, W. E., Malloci, G., Ballée, A., Kirchner, M. A., Moshfegh, P., Herledan, A., Herrmann, A., Deprez, B., Willand, N., Vargiu, A. V., Pos, K. M., Flipo, M., & Hartkoorn, R. C. (2022). Pyridylpiperazine-based allosteric inhibitors of RND-type multidrug efflux pumps. Nature Communications, 13(1), 1–11. https://doi.org/10.1038/s41467-021-27726-2

    Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W. (2014). Computational methods in drug discovery. Pharmacological Reviews, 66(1), 334–395. https://doi.org/10.1124/pr.112.007336

    Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackerman, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J. (2020). A Deep Learning Approach to Antibiotic Discovery. Cell, 180(4), 688-702.e13. https://doi.org/10.1016/j.cell.2020.01.021

    Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., . . . Bloom-Ackermann, Z. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688-702. e613. doi:https://doi.org/10.1016/j.cell.2020.01.021

    Tabassum, R., Kousar, S., Mustafa, G., Jamil, A., & Attique, S. A. (2023). In Silico Method for the Screening of Phytochemicals against Methicillin-Resistant Staphylococcus Aureus. BioMed Research International, 2023. https://doi.org/10.1155/2023/5100400

    Twana Salih, & Hawzhin A. Salih. (2020). In Silico Design and Molecular Docking Studies of Carbapenem Analogues Targeting Acinetobacter baumannii PBP1A Receptor. Al Mustansiriyah Journal of Pharmaceutical Sciences, 20(3), 35–50. https://doi.org/10.32947/ajps.v20i3.759

    WHO. (2019). 2019 Antibacterial Agents.

    Wong, F., Zheng, E. J., Valeri, J. A., Donghia, N. M., Anahtar, M. N., Omori, S., . . . Jin, W. (2024). Discovery of a structural class of antibiotics with explainable deep learning. Nature, 626(7997), 177-185. doi:https://doi.org/10.1038/s41586-023-06887-8

    Zhu, J., Zhang, H., Jia, L., Ma, L., Xu, L., Chen, Y., Cai, Y., Li, H., Huang, G., & Jin, J. (2021). Discovery of potential inhibitors targeting the kinase domain of polynucleotide kinase/phosphatase (PNKP): Homology modeling, virtual screening based on multiple conformations, and molecular dynamics simulation. Computational Biology and Chemistry, 94, 107517. https://doi.org/10.1016/J.COMPBIOLCHEM.2021.107517

  1. Abdelaziz, N. A., Elkhatib, W. F., Sherif, M. M., Abourehab, M. A. S., Al-Rashood, S. T., Eldehna, W. M., Mostafa, N. M., & Elleboudy, N. S. (2022). In Silico Docking, Resistance Modulation and Biofilm Gene Expression in Multidrug-Resistant Acinetobacter baumannii via Cinnamic and Gallic Acids. Antibiotics, 11(7). https://doi.org/10.3390/antibiotics11070870
  2. Amin, S., Andini, M., Alfarizi, A., & Pratomo, D. W. (2025). Eksplorasi Molekuler Docking Senyawa Antivirus terhadap Target Protein SARS-CoV-2?: Peran Umifenovir sebagai Kandidat Terapeutik dalam Kimia Medisinal COVID-19. 5(3), 17–26.
  3. Amin, S., & Nabila, L. S. (n.d.). Review artikel?: Peran Pendekatan In Silico Dalam Kimia Medisinal. 1(6), 1345–1349.
  4. Amin, S., Azijah, R. N., & Gunawan, F. R. (2024). Eksplorasi Senyawa Alami sebagai Lead Antikanker Payudara dengan Pendekatan In Silico. Jurnal Ilmu Medis Indonesia, 4(1), 63-74. doi:10.35912/jimi.v4i1.4560
  5. Amin, S., Hurry, Z. A. Z., Sumantri, T. A., & Fauzi, R. A. (2024). Studi Komputasional Senyawa Flavonoid Tanaman Obat sebagai Kandidat Agen Antidiabetik. Jurnal Ilmu Medis Indonesia, 4(1), 21-40. doi:10.35912/jimi.v4i1.4553
  6. Amin, S., Pujiyani, D., Rusiyana, N. P., & Azzahra, S. M. (2024). Evaluasi Potensi Antikanker Senyawa Daun Kelor melalui Kimia Medisinal. Jurnal Ilmu Medis Indonesia, 4(1), 75-82. doi:10.35912/jimi.v4i1.4544
  7. Amin, S., Supriatna, G. T., Ardian, M. I., & Abdurrahman, M. I. (2024). Potensi Senyawa Turunan Terpenoid sebagai Agen Anti-Kanker. Jurnal Ilmu Medis Indonesia, 4(1), 53-61. doi:10.35912/jimi.v4i1.4551
  8. Amin, S., Wihdatunnisa, I., Aisyah, R., & Kurniawan, Y. S. (2024). Potensi Senyawa Kuersetin sebagai Antikanker Payudara melalui Pendekatan Molecular Docking. Jurnal Ilmu Medis Indonesia, 4(1), 41-51. doi:10.35912/jimi.v4i1.4565
  9. Amon, D., Manu, P., Asante-Kwatia, E., Mante, P. K., Danquah, C. A., Borquaye, L. S., & Ekuadzi, E. (2024). Antimicrobial resistance modifying effects and molecular docking studies of Affinine, derived from Tabernaemontana crassa. Scientific African, 26, e02382. doi:https://doi.org/10.1016/j.sciaf.2024.e02382
  10. Cardona, S. T., Rahman, A. Z., & Novomisky Nechcoff, J. (2025). Innovative perspectives on the discovery of small molecule antibiotics. npj Antimicrobials and Resistance, 3(1), 19. doi:https://doi.org/10.1038/s44259-025-00089-0
  11. Ekins, S., Mestres, J., & Testa, B. (2007). In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. British Journal of Pharmacology, 152(1), 9–20. https://doi.org/10.1038/sj.bjp.0707305
  12. Jiménez-Osés, G., Osuna, S., Gao, X., Sawaya, M. R., Gilson, L., Collier, S. J., . . . Houk, K. (2014). The role of distant mutations and allosteric regulation on LovD active site dynamics. Nature chemical biology, 10(6), 431-436. doi:https://doi.org/10.1038/nchembio.1503
  13. Juki?, M., & Bren, U. (2022). Machine Learning in Antibacterial Drug Design. Frontiers in Pharmacology, 13(May), 1–11. https://doi.org/10.3389/fphar.2022.864412
  14. Kakoty, V., Kalarikkal Chandran, S., Gulati, M., Goh, B. H., Dua, K., & Kumar Singh, S. (2023). Senolytics: Opening avenues in drug discovery to find novel therapeutics for Parkinson’s Disease. Drug Discovery Today, 28(6), 103582. https://doi.org/10.1016/J.DRUDIS.2023.103582
  15. Kleandrova, V. V., Cordeiro, M. N. D. S., & Speck-Planche, A. (2025). In Silico Approach for Antibacterial Discovery: PTML Modeling of Virtual Multi-Strain Inhibitors Against Staphylococcus aureus. Pharmaceuticals, 18(2), 1–23. https://doi.org/10.3390/ph18020196
  16. Lionta, E., Spyrou, G., Vassilatis, D., & Cournia, Z. (2014). Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances. Current Topics in Medicinal Chemistry, 14(16), 1923–1938. https://doi.org/10.2174/1568026614666140929124445
  17. Plé, C., Tam, H. K., Vieira Da Cruz, A., Compagne, N., Jiménez-Castellanos, J. C., Müller, R. T., Pradel, E., Foong, W. E., Malloci, G., Ballée, A., Kirchner, M. A., Moshfegh, P., Herledan, A., Herrmann, A., Deprez, B., Willand, N., Vargiu, A. V., Pos, K. M., Flipo, M., & Hartkoorn, R. C. (2022). Pyridylpiperazine-based allosteric inhibitors of RND-type multidrug efflux pumps. Nature Communications, 13(1), 1–11. https://doi.org/10.1038/s41467-021-27726-2
  18. Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W. (2014). Computational methods in drug discovery. Pharmacological Reviews, 66(1), 334–395. https://doi.org/10.1124/pr.112.007336
  19. Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackerman, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J. (2020). A Deep Learning Approach to Antibiotic Discovery. Cell, 180(4), 688-702.e13. https://doi.org/10.1016/j.cell.2020.01.021
  20. Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., . . . Bloom-Ackermann, Z. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688-702. e613. doi:https://doi.org/10.1016/j.cell.2020.01.021
  21. Tabassum, R., Kousar, S., Mustafa, G., Jamil, A., & Attique, S. A. (2023). In Silico Method for the Screening of Phytochemicals against Methicillin-Resistant Staphylococcus Aureus. BioMed Research International, 2023. https://doi.org/10.1155/2023/5100400
  22. Twana Salih, & Hawzhin A. Salih. (2020). In Silico Design and Molecular Docking Studies of Carbapenem Analogues Targeting Acinetobacter baumannii PBP1A Receptor. Al Mustansiriyah Journal of Pharmaceutical Sciences, 20(3), 35–50. https://doi.org/10.32947/ajps.v20i3.759
  23. WHO. (2019). 2019 Antibacterial Agents.
  24. Wong, F., Zheng, E. J., Valeri, J. A., Donghia, N. M., Anahtar, M. N., Omori, S., . . . Jin, W. (2024). Discovery of a structural class of antibiotics with explainable deep learning. Nature, 626(7997), 177-185. doi:https://doi.org/10.1038/s41586-023-06887-8
  25. Zhu, J., Zhang, H., Jia, L., Ma, L., Xu, L., Chen, Y., Cai, Y., Li, H., Huang, G., & Jin, J. (2021). Discovery of potential inhibitors targeting the kinase domain of polynucleotide kinase/phosphatase (PNKP): Homology modeling, virtual screening based on multiple conformations, and molecular dynamics simulation. Computational Biology and Chemistry, 94, 107517. https://doi.org/10.1016/J.COMPBIOLCHEM.2021.107517

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