"Automated Inference of Chemical Discriminants of Biological Activity" Code Repository

Complimentary dataset and code for the chapter

"Automated Inference of Chemical Discriminants of Biological Activity"

in the book "Computational drug discovery and design"* (Methods in Molecular Biology, Springer Protocols).

Authors:
Sebastian Raschka, Anne M. Scott, Mar Huertas, Weiming Li, and Leslie A. Kuhn

Requirements

Python Interpreter

To run the code examples, a recent version of Python is required (3.5 or newer) is required; Python 3.6 is recommended. You can https://www.python.org/downloads/.

Python Libraries

The following list specifies the Python libraries used in this chapter, the recommended version number, and a short description of their use:

• NumPy version 1.13.0 or newer (http://www.numpy.org); numerical array library
• SciPy version 0.19.0 or newer (https://www.scipy.org); advanced functions for scientific computing
• Pandas version 0.20.1 or newer (http://pandas.pydata.org); handling of CSV files and working with data frames
• Matplotlib version 2.0.2 or newer (https://matplotlib.org); 2D plotting
• Scikit-learn version 0.18.1 or newer (http://scikit-learn.org/stable/); algorithms for machine learning
• MLxtend version 0.7.0 or newer (http://rasbt.github.io/mlxtend/); sequential feature selection algorithms

The scientific computing libraries listed above can be installed using Python's in-built Pip module by executing the following line of code directly from a macOS/Unix, Linux, or Windows MS-DOS terminal command line:

pip install numpy scipy pandas matplotlib scikit-learn pydotplus mlxtend

If you encounter problems with version incompatibilities, you can specify the package versions explicitly, as shown in the following terminal command example:

pip install numpy==1.13.0 scipy==0.19.0 pandas==0.20.1 matplotlib==2.0.2 scikit-learn==0.18.1 pydotplus==2.0.2 mlxtend==0.7.0

Graph Visualization Software

To visualize the decision trees later in this chapter, an installation of GraphViz is needed. The GraphViz downloader is freely available at http://www.graphviz.org with the installation and setup instructions.

Jupyter Notebook

To open and execute the code in the file code/dkpes_fgroup_analysis.ipynb locally, Jupyter Notebook for Python is required. For more information on installing Jupyter Notebook, please visit http://jupyter.readthedocs.io/en/latest/install.html.

Alternatively, if you don't want to install Jupyter Notebook, you can view the code in your browser by clicking on the code/dkpes_fgroup_analysis.ipynb file in this GitHub repository.