โก Quick Summary
This study presents a groundbreaking machine learning-assisted affinity ultrafiltration (ML-AAUF) strategy for the rapid screening of bioactive natural products, specifically identifying neuraminidase inhibitors from medicinal herbs. The approach successfully identified nine compounds, including resveratrol, which demonstrated significant antiviral activity against H1N1 PR8 with an IC50 of 16.8 ฮผM.
๐ Key Details
- ๐ฑ Focus: Screening of neuraminidase inhibitors from medicinal herbs
- ๐ฌ Methodology: Machine learning-assisted affinity ultrafiltration (ML-AAUF)
- ๐ฟ Plant Sources: Polygonum cuspidatum and Lonicera japonica
- โ๏ธ Identified Compounds: Nine compounds with neuraminidase inhibitory activity
- ๐ Notable Compound: Resveratrol with an IC50 of 16.8 ฮผM
๐ Key Takeaways
- ๐ก Novel Strategy: ML-AAUF integrates machine learning with affinity ultrafiltration for efficient natural product discovery.
- ๐ Diverse Inhibitors: Identified various structural types of neuraminidase inhibitors beyond flavonoids.
- ๐ Resveratrol: Exhibited significant antiviral properties, indicating its potential as an anti-influenza agent.
- โก Rapid Screening: The ML-AAUF method allows for quicker identification of high-quality bioactive compounds.
- ๐ Implications: This approach could transform the discovery process for bioactive natural products in medicinal research.
๐ Background
The discovery of bioactive natural products is crucial for developing new treatments for various human diseases. Traditional methods, such as bioactivity-guided isolation, are often labor-intensive and yield low hit rates. Conversely, affinity-based ligand fishing, while faster, frequently results in compounds that are structurally similar and exhibit weak bioactivity. This highlights the need for innovative strategies that can enhance the efficiency and effectiveness of natural product discovery.
๐๏ธ Study
The study introduces the machine learning-assisted affinity ultrafiltration (ML-AAUF) strategy, which combines the predictive power of machine learning with the specificity of affinity ultrafiltration. By training machine learning models to explore chemical spaces, researchers were able to identify promising bioactive compounds from medicinal herbs, specifically targeting neuraminidase inhibitors.
๐ Results
Utilizing the ML-AAUF strategy, the researchers successfully identified nine compounds with neuraminidase inhibitory activity. Among these, resveratrol stood out, demonstrating significant antiviral activity against H1N1 PR8, with an IC50 of 16.8 ฮผM. The study also uncovered other structural types of neuraminidase inhibitors, including stilbene, anthraquinone, and phenolic acid compounds, showcasing the method’s versatility.
๐ Impact and Implications
The introduction of the ML-AAUF strategy marks a significant advancement in the field of natural product discovery. By integrating machine learning with rapid screening techniques, this approach not only enhances the efficiency of identifying high-quality bioactive compounds but also broadens the scope of potential therapeutic agents. The implications for medicinal research are profound, as this method could lead to the discovery of novel treatments for various diseases, particularly viral infections.
๐ฎ Conclusion
This study highlights the transformative potential of machine learning in the discovery of bioactive natural products. The ML-AAUF strategy offers a rapid and targeted approach to identifying high-quality compounds, paving the way for future research and development in medicinal herbs. As we continue to explore the intersection of technology and natural product discovery, the future looks promising for innovative therapeutic solutions.
๐ฌ Your comments
What are your thoughts on the integration of machine learning in natural product discovery? We would love to hear your insights! ๐ฌ Share your comments below or connect with us on social media:
Machine learning-assisted affinity ultrafiltration for bioactive natural products discovery:Application to screening of neuraminidase inhibitors from medicinal herbs.
Abstract
BACKGROUND: Bioactive natural products represent a vital resource for combating human diseases. However, their discovery often encounters multiple challenges. Bioactivity-guided isolation can yield bioactive compounds but are labor-intensive and have a low hit rate. In contrast, affinity-based ligand fishing enables rapid screening; however, the identified compounds are often structurally monotonous and exhibit weak bioactivity. Therefore, there is an urgent need for a novel strategy for rapid and targeted discovery of high-quality bioactive natural products.
RESULTS: This study introduces a novel machine learning-assisted affinity ultrafiltration (ML-AAUF) strategy for the screening of bioactive natural products. Machine learning models were first trained and introduced to explore the chemical spaces. Using ML-AAUF, we identified nine compounds with neuraminidase inhibitory activity from Polygonum cuspidatum and Lonicera japonica. Flavonoids are common natural inhibitors of neuraminidase. In this study, three other structural types of neuraminidase inhibitors, including stilbene compounds, anthraquinone compounds and phenolic acid compounds containing ester bonds were also identified. Notably, resveratrol exhibited significant antiviral activity against H1N1 PR8, with an IC50 of 16.8ย ฮผM, highlighting its potential as an anti-influenza agent.
SIGNIFICANCE: Machine learning-assisted affinity ultrafiltration strategy was first proposed. This strategy integrates machine learning’s predictive capabilities with the rapidity and specificity of affinity ultrafiltration, offering a rapid and targeted approach to high-quality natural product discovery.
Author: [‘Chen M’, ‘Ding S’, ‘Wang L’, ‘Jian T’, ‘Yang Y’, ‘Rong R’]
Journal: Anal Chim Acta
Citation: Chen M, et al. Machine learning-assisted affinity ultrafiltration for bioactive natural products discovery:Application to screening of neuraminidase inhibitors from medicinal herbs. Machine learning-assisted affinity ultrafiltration for bioactive natural products discovery:Application to screening of neuraminidase inhibitors from medicinal herbs. 2025; 1374:344522. doi: 10.1016/j.aca.2025.344522