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Updated: Apr 6, 2026
A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis
Published on: March 8, 2022
Integrative Machine Learning and Network Pharmacology Approach to Uncover Phytochemical Therapeutics for Melanoma and
Madhu Anabala1, V Vanitha Jain1, Deepak Sharma1
1School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
Introduction:
Melanoma and Breast cancer are the leading cancers that cause thousands of deaths annually worldwide. Although several early identification methods and treatments are being used, they have limitations, such as drug resistance and off-target effects.
Methods:
To find dual-acting inhibitors for the MDA-MB-435 lineage, we describe a novel integrative approach that combines bioinformatics-driven phytochemical screening with machine learning-based QSAR modeling. This will enable prioritizing natural chemicals with therapeutic promise against both diseases quickly and economically.
Results:
Among all the models developed, the Extra Trees and Random Forest classifiers performed well, achieving 95% accuracy in identifying the bioactivity class (IC50) of phytochemicals. Furthermore, molecular docking studies revealed that Ombuin outperformed all other test ligands, with docking scores of -5.5 kcal/mol for IL6 and -6.8 kcal/mol for JUN. MDS were performed for the IL6_Ombuin, JUN_Ombuin, IL6_Reference, and JUN_Reference complexes to better understand these interactions. Analysis of RMSD, RMSF, RG, SASA, and Hydrogen bonds for IL6_Ombuin showed lower RMSD and RMSF than for the reference ligand, indicating greater stability.
Discussion:
The IL6_Ombuin complex exhibited consistent hydrogen bond formation, indicating better structural stability.
Conclusion:
The study's complete pipeline identified Ombuin as a potential hit compound. In the future, in vitro and in vivo studies should be conducted to confirm its therapeutic potential against melanoma and breast cancer.
Insights
This study identified Ombuin as a promising natural compound for treating melanoma and breast cancer. Computational methods prioritized Ombuin for its potential dual-acting inhibitory effects and structural stability.
Area of Science:
- Computational chemistry
- Cheminformatics
- Drug discovery
Background:
- Melanoma and breast cancer are leading causes of cancer deaths globally.
- Current treatments face limitations like drug resistance and off-target effects.
Purpose of the Study:
- To develop a novel, cost-effective computational approach for identifying dual-acting inhibitors against melanoma and breast cancer.
- To prioritize natural compounds with therapeutic potential using bioinformatics and machine learning.
Main Methods:
- Integrated bioinformatics-driven phytochemical screening with Quantitative Structure-Activity Relationship (QSAR) modeling.
- Utilized Extra Trees and Random Forest classifiers, achieving 95% accuracy in predicting phytochemical bioactivity.
- Performed molecular docking and molecular dynamics (MD) simulations to assess binding interactions and stability.
Main Results:
- Ombuin demonstrated superior binding affinity compared to reference ligands for IL6 and JUN targets.
- Molecular docking revealed strong inhibitory potential, with Ombuin achieving scores of -5.5 kcal/mol (IL6) and -6.8 kcal/mol (JUN).
- MD simulations indicated enhanced structural stability of the IL6-Ombuin complex, evidenced by lower RMSD and RMSF values.
Conclusions:
- Ombuin emerged as a potential hit compound with dual-acting inhibitory properties against melanoma and breast cancer.
- The IL6-Ombuin complex exhibited stable hydrogen bond formation, suggesting favorable drug-like characteristics.
- Further in vitro and in vivo studies are recommended to validate Ombuin's therapeutic efficacy.
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