Version 1
: Received: 23 June 2023 / Approved: 26 June 2023 / Online: 26 June 2023 (10:36:43 CEST)
How to cite:
Romdendine, M. F.; Kusuma, W. A.; Annisa, A.; Nurilmala, M.; Fitriani, R. Unleashing Black Sea Cucumber's Potential: Identifying Anti-Cancer Compounds Through In Silico Drug Discovery. Preprints2023, 2023061793. https://doi.org/10.20944/preprints202306.1793.v1
Romdendine, M. F.; Kusuma, W. A.; Annisa, A.; Nurilmala, M.; Fitriani, R. Unleashing Black Sea Cucumber's Potential: Identifying Anti-Cancer Compounds Through In Silico Drug Discovery. Preprints 2023, 2023061793. https://doi.org/10.20944/preprints202306.1793.v1
Romdendine, M. F.; Kusuma, W. A.; Annisa, A.; Nurilmala, M.; Fitriani, R. Unleashing Black Sea Cucumber's Potential: Identifying Anti-Cancer Compounds Through In Silico Drug Discovery. Preprints2023, 2023061793. https://doi.org/10.20944/preprints202306.1793.v1
APA Style
Romdendine, M. F., Kusuma, W. A., Annisa, A., Nurilmala, M., & Fitriani, R. (2023). Unleashing Black Sea Cucumber's Potential: Identifying Anti-Cancer Compounds Through In Silico Drug Discovery. Preprints. https://doi.org/10.20944/preprints202306.1793.v1
Chicago/Turabian Style
Romdendine, M. F., Mala Nurilmala and Rizka Fitriani. 2023 "Unleashing Black Sea Cucumber's Potential: Identifying Anti-Cancer Compounds Through In Silico Drug Discovery" Preprints. https://doi.org/10.20944/preprints202306.1793.v1
Abstract
Despite being an abundant marine organism in Indonesia, black sea cucumber is still underutilised due to its slightly bitter taste. Previous studies have hinted at the potential of black sea cucumber as an anti-cancer agent. However, specific identification of bioactive compounds that can interact with cancer proteins is still lacking. In the same place, cancer ranks third as Indonesia's leading cause of death. Therefore, this study aims to identify potential anti-cancer compounds from black sea cucumbers using a comprehensive in silico drug discovery approach. This research uses machine learning, molecular docking, and ADMET analysis to identify bioactive compounds that specifically interact with cancer proteins. A combination of the Cascade Deep Forest algorithm and ECFP-AAIndex1 feature combination proved to be the most effective in predicting these interactions. Through molecular docking validation, four bioactive compounds with strong binding affinity were identified: Afimoxifene, Danazol, Taxifolin, and Terfenadine. ADMET analysis highlighted Taxifolin as the most promising candidate, as it passed most ADMET parameters. Further wet laboratory studies are required to confirm the effects and potential of these compounds as anti-cancer agents. This study builds a foundation for future investigations into alternative cancer treatments using abundant natural resources.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
