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Molecular Descriptor Calculator for QSAR and Machine Learning
PyDescriptorC*: A Brief idea
PyDescriptorC*: A Brief idea
PyDescriptorC* is a Python-based extension of PyDescriptor (https://doi.org/10.1016/j.chemolab.2017.08.003)
Provides interpretable and reproducible descriptors directly in CSV format.
Validated on diverse datasets, PyDescriptorC* provides interpretable descriptors and deep information often missed by traditional descriptor tools.
Broad descriptor coverage: constitutional, topological, geometric/spatial, circular fingerprints, quantum chemical, and chirality-specific descriptors. It computes 112,194 molecular descriptors, including 15,150 chirality-aware descriptors (~13.5%).
PyDescriptorC* is a user-supported application. We rely on your contributions to maintain and improve it. Please consider supporting us by sending a sponsorship amount of your choice. There is no minimum or maximum limit for sponsorship; however, contributing is mandatory to continue using the application.
Just upload your .zip or .rar file containing all molecules in mol2 format using the below Google form. Size limit is 100 MB for zip or rar file. Use a separate mol2 file for each molecule. Please fill the following Google form to upload your molecules- no other data required:
Upload your molecules here to use PyDescriptorC*.
Developers:
Developers:
The platform is jointly developed by researchers from India, Saudi Arabia, and Croatia.
Vidya Bharati Mahavidyalaya, India
Dr. D. Y. Patil Institute of Technology, India
Dr. Sami A. Al-Hussain
Imam Mohammad Ibn Saud Islamic University, Suadi Arabia
Dr. Rahul D. Jawarkar
R. Gode Institute of Pharmacy, India
J.J.S. University of Osijek, Croatia
Dr. Magdi E.A. Zaki
Imam Mohammad Ibn Saud Islamic University, Suadi Arabia
Reference:
Reference:
If you find the platform useful in your research, please cite the following article:
Masand, VH, Masand, GS, Al-Hussain, SA, Jawarkar, RD, Rastija, V. and Zaki, MEA (2025) PyDescriptorC*: A Descriptor Calculation Tool for Decoding Chirality Cliffs and Revealing Hidden Patterns in Drug Discovery. RHAZES: Green and Applied Chemistry, 21, 32 - 51. https://doi.org/10.26434/chemrxiv-2025-w3k4n
Users' opinion on PyDescriptorC*:
Users' opinion on PyDescriptorC*:
"I take the opportunity to provide feedback on PyDescriptorC*. In QSAR studies, descriptors are the heart of model building, & essential for generating accurate and robust equations. What stands out about PyDescriptorC* is that which covers all types of properties, especially stereochemical, this feature makes it valuable for comprehensive QSAR studies. Additionally, descriptors are facilitating mechanistic interpretations without requiring prior extensive knowledge about chemistry, making it user-friendly. The prompt responses and excellent support provided further enhance the overall experience. I believe this tool can play a crucial role for researchers in the field. I look forward to incorporating it into my projects and seeing how it continues to evolve. "
Regards,
Dr. Somdatta Y. Chaudhari, Modern College of Pharmacy, Nigdi, Pune, India
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