MDM2pred is a powerful machine learning tool for predicting the inhibitory potency of compounds against the human E3 ubiquitin ligase MDM2, a key regulator of the tumor suppressor p53. Based on the KNeighbors Regressor algorithm, MDM2pred has been trained on a comprehensive dataset of 1647 known MDM2 inhibitors, achieving an impressive R² value of ~0.74 and an RMSE of ~0.70 (in pIC50 units) over a 10-fold cross-validation. By simply inputting the SMILE notation of any compound, MDM2pred predicts its pIC50 value against MDM2 and returns the result as IC50. MDM2pred can be a valuable resource for researchers and drug developers looking to accelerate their early screening steps.
Naeem Abdul Ghafoor¹ & Ayşegül Yildiz¹² @Yildiz Neuro Lab
¹Department of Molecular Biology and Genetics, Graduate School of Natural and Applied Sciences, Mugla Sitki Kocman University, 48000 Mugla, Turkey
³Department of Molecular Biology and Genetics, Faculty of Science, Mugla Sitki Kocman University, Mugla, Turkey.
- To use MDM2pred, simply click on the "Open in Streamlit" badge above.
- Enter the SMILES notation for your compound of interest.
- Within seconds, MDM2pred will generate a 2D depiction of the compound and its predicted IC50 value against MDM2.
- Use this information to gain valuable insights into the inhibitory potential of your compound and accelerate your drug development efforts.
Download the project
wget https://github.com/naeemmrz/mdm2pred.gitUnzip and enter the project directory
unzip mdm2pred-main.zip
cd mdm2pred-mainInstall dependencies
pip install -r requirements.txtRun the Reproduce.py
python Reproduce.pyThe results will be printed out in the terminal.
Download the project
wget https://github.com/naeemmrz/mdm2pred.gitUnzip and enter the project directory
unzip mdm2pred-main.zip
cd mdm2pred-mainInstall dependencies
pip install -r requirements.txtStart the application
streamlit run MDM2pred.pyThe Application will open in your default browser with the same interface as the online version.
The development of MDM2pred was funded by the Research Support and Funding Office (BAP) of Mugla Sitki Kocman University under the project number 22/138/01/3/4.