TestBio

Understanding Phytocomplex Bioactivity

Medicinal plants are often studied for their potential health benefits. However, fully understanding the combined effects of the numerous compounds present in plant extracts, known as phytocomplexes, can be a challenge. TestBio offers a rapid, in silico method to estimate the potential bioactivity of a phytocomplex, opening new perspectives in research.

How TestBio Works

TestBio compares the molecular structures of the phytocomplex components with a library of known bioactive compounds, identifying potential targets and suggesting possible bioactivities. The process involves several steps:

• Molecule Input: Enter the molecules of the botanical species in SMILES format, one per line, in the appropriate text area.
• Fingerprint Calculation: TestBio calculates the molecular fingerprints of the entered compounds.
• Comparison with the Library: The fingerprints are compared with a library of 1700 compounds with known biological activity and protein targets, using the Tanimoto coefficient. This produces a value between zero and one, where 1 represents identity.
• Results Analysis: For each molecule, a ranking of the compounds with the highest similarity is generated, producing a DataFrame containing the similarity scores, target information, and bioactivities for each comparison.

Data Visualization

The obtained data is analyzed and visualized through four types of interactive graphs, offering a deeper understanding of the interactions between the compounds and their potential targets:

• Scatter Plot: Displays the distribution of Tanimoto similarity scores for each compound in the phytocomplex.
• Bar Chart: Shows the frequency of the different protein targets associated with the compounds.
• Box Plot: Analyzes the distribution of Tanimoto similarity scores across different targets.
• Network Visualization: Graphically depicts the interactions between the compounds in the phytocomplex and the potential target molecules.

Important Notes

TestBio utilizes Python libraries such as pandas, RDKit, matplotlib, and plotly for data processing, fingerprint analysis, and visualizations.

Disclaimer: This tool is provided for research purposes only and should not be used to provide medical advice.

Further Information

For further information, please consult the tutorials in the sidebar.

This website's code? Gemini 2.0 Flash and I make a pretty good team, if I do say so myself.

You can find more about my projects and research on my personal website: Alessandro-Project

For inquiries, please contact me at: alessandrocareglio@gmail.com