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QSPRpred

QSPRpred is open-source software libary for building **Quantitative Structure Property Relationship (QSPR)** model developed by Gerard van Westen's Computational Drug Discovery group. 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 >= 3.8.0):

pip install git+https://github.com/CDDLeiden/QSPRPred.git@main

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 the following installation options:

# include deep neural network models and their dependencies
pip install git+https://github.com/CDDLeiden/QSPRPred.git@main#egg=qsprpred[deep] 

# include extra dependencies for PCM models and extra descriptor sets from packages other than RDKit
pip install git+https://github.com/CDDLeiden/QSPRPred.git@main#egg=qsprpred[extra]

# include all optional dependencies
pip install git+https://github.com/CDDLeiden/QSPRPred.git@main#egg=qsprpred[full]

Multiple Sequence Alignment Provider for Protein Descriptors

If you plan to optionally use QSPRPred to calculate protein descriptors for PCM, make sure to also install Clustal Omega. You can get it via conda:

conda install -c bioconda clustalo

This is needed to provide multiple sequence alignments for the PCM descriptors. Clustal Omega does not, however, work on Windows. In that case, you can use MAFFT instead:

conda install -c biocore mafft

Use

After installation, you will have access to various command line features, but you can also use the Python API directly (see Documentation). For a quick start, you can also check out the Jupyter notebook tutorials, which documents the use of the Python API to build different types of models. This tutorial shows how a QSAR model can be trained. This tutorial shows how to use a QSAR model to predict the bioactivity of a set of molecules. The tutorials as well as the documentation are still work in progress, and we will be happy for any contributions where it is still lacking.

To use the commandline to train the same QSAR model as in the tutorial use (run from tutorial folder):

python -m qsprpred.data_CLI -i parkinsons_pivot.tsv -pr GABAAalpha -r true -sf 0.15 -fe Morgan
python -m qsprpred.model_CLI -pr GABAAalpha -r true -m PLS -s -o bayes -nt 10 -me

Workflow

image

Current Development Team