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Registering the RDKit environment with Jupyterįirst, deactivate the current RDKit environment created in the previous step with the following command conda deactivate Now we have verified that the RDKit environment has installed correctly let’s register the environment with Jupyter. Note that the information for the object after the “at” text will be different on your system. The final line will display the type of the m object. Type the following commands: > from rdkit import rdBase pythonįollowing the execution of the python command, any subsequent line is interpreted as commands to the python environment.
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To enable the code entry at the command line type. To test the installation, we are going to enter python commands directly into the command line.
To activate the RDKit environment enter the following command. Once the environment creation is complete, we can activate the environment and test the installation. At the command prompt type the following command conda create -c rdkit -n my-rdkit-env rdkit Once the RDKit libraries have installed, you need to create an RDKit environment. If at this point you receive an “Access Denied” error message close the command prompt and re-open the command prompt as an Administrator. To install RDKit at the Anaconda command prompt type the following command: conda install -c rdkit rdkit If Anaconda installed correctly the version should be displayed I am using version 4.5.4. If the Anaconda Command prompt is not listed check that you have the Visual Studio Data Science Workload installed.
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Once the Data Science Workload, to open the Anaconda Prompt in the Windows 10 search bar enter “Anaconda” and select the Anaconda Command prompt. The Data Science workload includes Anaconda and a local instance of the Jupyter Notebook, which is ideal for analyzing datasets in Python.įor setting RDKit up with Jupyter, we are going to use the version of Anaconda that ships with Visual Studio 2017.5.įrom the Visual Studio 2017 installer, install the Data Science Workload, ensuring the Anaconda install option is selected. Visual Studio 2017 includes many different workloads for different functions. To install RDKit, I am using Anaconda, which is a command-line installation tool for Python libraries. Therefore it would seem to a good fit to use RDKit to visualize a structure inline within the Jupyter Notebook.
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RDKit is an open-source cheminformatics software toolkit which can be called from Python and includes API’s to generate SVG representations of chemical structures. When analyzing data that has an associated molecular structure, it would be useful to visualize the structure within the current Jupyter notebook page rather than finding the record in the dataset and copy/pasting the raw structure format into another application. The Jupyter Notebook is an open-source web application that allows the creation and sharing of documents that can contain embedded Python code, and, data visualizations in SVG (Scalable Vector Graphics), and text.