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- This Software was made with an intention to make QSAR/QSPR development more efficient and reproducible.
- Published in ACS, Journal of Chemical Information and Modeling . Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372
- Simple to use and no compromise on essential features necessary to make reliable QSAR models.
- From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp.
For any feedback or queries, contact
kabeermuzammil614@gmail.com
- Available on Windows and Linux
- Software Authorship - Muzammil Kabier
-If You are Facing Issues in Deployment to Streamlit, Try 'requirements.txt' in the Github repo or The Files Deposited Here.
Features
- Extraction of Data From ChEMBL Database or Users In-House Data
- Automatic Processing of Y-Label Depending TASK(Regression\Classification) according to users preferences
- Defining Applicability Domain (FP - Tanimoto Similarity & Descriptors - Boundary Box)
- Comparing Data in different ML Models with Metrics (R2/RMSE) or MCC with setting K-Fold split and Low variance threshold
- Contains 22 ML Models and 16 FP from PadelPy, RDkit and CSFP with RDkit Molecular Descriptors
- Choose ML model or Descriptor/FP of choice to build model (Automatic Internal Validation(R2/RMSE & Accuracy, Sensitivity, Specificity and MCC))
- Convert QSAR model to WebApp (Automatic External Validation)
- Upload the App Folder Files in GitHub Repository , and link to Streamlit to launch your own QSAR app
- Sequential Feature Selection (Forward Direction)
Project Samples
