Predicting Drug-Target Interaction (DTI) based on Machine Learning with Lasso Dimensionality Reduction and SMOTE from Protein Sequence and Drug Fingerprint

Calangian, Maria Theresa F.; Magboo, Vincent Peter C.

doi:10.1109/icecet55527.2022.9873060

Cited by 1 publication

(2 citation statements)

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“…The process of identifying new drugs has been transformed by introducing cutting-edge technologies, including machine learning, molecular docking, and ADMET prediction (Absorption, Distribution, Metabolism, Excretion, and Toxicity). These cutting-edge techniques have greatly improved the discovery and development of prospective medicinal medicines [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, in silico drug discovery encompassing drug-target interactions, molecular docking, and ADMET analysis has gained significant importance in developing innovative cancer therapies. For instance, a study focusing on breast cancer utilized molecular docking and in vitro techniques to expedite drug discovery, demonstrating a strong consistency between the two approaches [8]. Another research investigated the in silico drug design of anti-breast cancer agents, encompassing molecular docking studies, MD simulations, and ADMET prediction [16].…”

Section: Introductionmentioning

confidence: 99%

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Unleashing Black Sea Cucumber's Potential: Identifying Anti-Cancer Compounds Through In Silico Drug Discovery

Romdendine,

Kusuma,

Annisa

et al. 2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%