February 28, 2025Journal of Student ResearchOpen Access

Machine Learning-Based Prediction of Biological Activity in Natural Product Phytochemicals

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Machine learning demonstrates classification of biological activity in phytochemicals, suggesting new pathways for drug discovery.

The model achieved an accuracy of 0.7926 and an F1-Score of 0.7248, indicating strong predictive performance.
Dataset categorized into Antioxidant, Toxicity, Anti-inflammatory, and Lipid Metabolism shows diverse applications.
Proposed bioactivity classification network deploys a machine learning approach with a depth of 4 layers for optimal results.
Findings support the potential for efficient drug discovery from natural product phytochemicals, highlighting innovation in therapeutic solutions.

Lee et al. (2025) studied this question.

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