The web page application for affinity prediction for serotonergic targets and properties (Blood-brain barrier penetration and Human intestinal absorption)
You can find the application at the following link: https://serotoninai.streamlit.app/
https://hub.docker.com/r/serotoninai/serotoninai-app
To run the application locally, follow these steps:
- Clone the repository
- Install dependencies:
The needed packages are in file enviroment.txt
During installation you create conda environment 'for_serotoninAI'
- Activate environment
In the console activate conda environment:
$ conda activate for_serotoninAI- Now, you can run the application:
$ streamlit run app_streamlit_SerotoninAI.pyApp should open in the browser or it will be available at 'http://localhost:8501'.
- Finally, have fun and test my app!
In batch mode, you can calculate predictions for multiple molecules. The online version of SerotoninAI has limitations based on Streamlit Cloud. The local app is much better to use for larger database.
Please, remember to upload CSV file with the column names 'smiles', because based on this system will predict affinity or property.
I'm Natalia Łapińska (maiden name Czub) and I'm the author of SerotoninAI
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GitHub: https://github.com/nczub
I would love to work with you if you want to collaborate on creating new QSAR or QSPR models.
- SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery
Natalia Łapińska, Adam Pacławski, Jakub Szlęk, and Aleksander Mendyk https://doi.org/10.1021/acs.jcim.3c01517
- Integrated QSAR Models for Prediction of Serotonergic Activity: Machine Learning Unveiling Activity and Selectivity Patterns of Molecular Descriptors
Natalia Łapińska, Adam Pacławski, Jakub Szlęk, and Aleksander Mendyk https://doi.org/10.3390/pharmaceutics16030349
This project is available under the GNU General Public License v3.0 (GPL-3.0).