QSPRpred is open-source software libary for building **Quantitative Structure Property/Activity
Relationship (QSPR/QSAR)** models developed by Gerard van Westen's Computational Drug
Discovery group. It provides a unified interface for building QSPR models based on
different types of descriptors and machine learning algorithms. We developed this
package to support our research, recognizing the necessity to reduce repetition in our
model building workflow and improve the reproducibility and reusability of our models.
In making this package available here, we hope that it may be of use to other
researchers as well. QSPRpred is still in active development, and we welcome
contributions and feedback from the community.
QSPRpred is designed to be modular and extensible, so that new functionality can be
easily added. A command line interface is available for basic use cases to quickly,
explore varying scenarios. For more advanced use cases, the Python API offers extra
flexibility and control, allowing more complex workflows and additional features.
Internally, QSPRpred relies heavily on the RDKit
and scikit-learn libraries. Furthermore,
for scikit-learn model saving and loading, QSPRpred
uses ml2json for safer and
interpretable model serialization. QSPRpred is also interoperable
with Papyrus, a large scale
curated dataset aimed at bioactivity predictions, for data collection. Models developed
with QSPRpred are compatible with the group's *de novo* drug design
package DrugEx.
Quick Start
===========
## Installation
QSPRpred can be installed with pip like so (with python >= 3.10):
```bash
pip install qsprpred
```
Note that this will install the basic dependencies, but not the optional dependencies.
If you want to use the optional dependencies, you can install the package with an
option:
```bash
pip install qsprpred[
