Setting up a Python environment for cheminformatics and data science in chemistry is an essential step for researchers who want to take advantage of the latest tools and techniques in the field. With the right setup, researchers can greatly enhance their productivity and accelerate their research progress.

Python is a popular programming language in the field of cheminformatics and data science in chemistry. It provides a wide range of libraries and tools that can be used for tasks such as data analysis, machine learning, and molecular modeling. Setting up a Python environment for cheminformatics and data science in chemistry can seem daunting at first, but it can greatly benefit researchers in the field by enabling them to perform complex analysis and modeling tasks efficiently.

In this setup process, researchers need to choose the right Python distribution, install the necessary libraries and tools, and configure their environment to meet their specific needs. Once set up, the Python environment can help researchers perform tasks such as molecular visualization, chemical rxn simulations, drug design, and dealing with MOFs. Additionally, it can help automate tedious data analysis tasks, allowing researchers to focus on the scientific questions at hand.

The first step in setting up your Python environment for cheminformatics is to ensure that Python is installed on your machine. There are several Python distributions available, but it is important to choose one that is best suited for data science. Anaconda is a popular choice because it includes its own Python distribution.

• Anaconda

• Anaconda is particularly well-suited for data science because it provides a range of scientific packages such as NumPy and SciPy, as well as data manipulation packages like Pandas, and interactive Jupyter Notebooks. In addition, Anaconda includes two tools: Conda for the Command Line Interface and Navigator for the Graphical User Interface. These tools allow for easy management of package versions and dependencies.

• By using Anaconda, you do not need to worry about installing individual Python packages as most of them come pre-installed. If a new package is needed, it can be easily installed using Conda or Pip. 

• Jupyter notebooks contain both code and rich text elements, such as figures, links, and equations. You can learn more about Jupyter Notebooks here.

Here is the step-by-step guide for Anaconda installation:

• for MacOS 

• for Windows

• for Linux

• How to open a Jupyter notebook

A way to open a Jupyter notebook is by using the Anaconda Navigator. You can open the Anaconda Navigator using the Windows Start Menu and selecting [Anaconda3(64-bit) Folder] → [Anaconda Navigator]. 

This will open the Anaconda Navigator. In the middle of the page, in the Jupyter notebook tile, click [Launch] 

This will open the Jupyter file browser in a web browser tab.

In the upper right select [New] → [Python 3]

A new notebook will open as a new tab in your web browser.

Congratulations! You know how to open a Jupyter notebook. Now go write some Python code to solve some problems!

Cloud notebooks are web-based environments that allow users to write and execute code, create visualizations, and collaborate with others in real-time. These notebooks run entirely in the cloud, which means that users do not need to install any software locally on their computers.

Cloud notebooks are particularly useful for data science and machine learning tasks because they provide easy access to powerful computing resources, such as GPUs and TPUs, that can accelerate the training of machine learning models. In addition, cloud notebooks can be easily shared and accessed from anywhere, which makes them ideal for collaborative work.

Some popular examples of cloud notebooks include Google Colab, Deepnote, and Paperspace Gradient. These platforms provide a range of features, such as pre-installed libraries and machine learning frameworks, that make it easy for users to get started with data analysis and modeling tasks. They also provide integrations with other cloud services, such as GitHub and Google Drive, which makes it easy to import and export data, collaborate with others, and deploy machine learning models to production environments.

Cloud notebooks are a powerful and convenient tool for data scientists and machine learning engineers who need to work with large amounts of data and collaborate with others in real-time. They provide access to powerful computing resources and enable researchers to work together more efficiently, which can accelerate research progress and lead to new discoveries.

Here, we are going to learn about the best features of the two best cloud notebooks, Google Colab and Deepnote. 

Google Colab is a cloud-based notebook environment that provides a range of features for data analysis, machine learning, and deep learning tasks. It is a free, collaborative tool that allows users to write and execute Python code in a web-based interface. 

• Features

• Integration with Google Drive: Google Colab integrates seamlessly with Google Drive, allowing users to easily upload and access data files directly from their Drive account. This makes it easy to work with large datasets and share data with others.

• Pre-installed libraries and frameworks: Google Colab comes with pre-installed libraries and frameworks that make it easy to get started with data analysis and machine learning tasks. For example, it includes popular libraries such as NumPy, Pandas, and Matplotlib, as well as machine learning frameworks such as TensorFlow and PyTorch.

• Collaboration: Google Colab provides powerful collaboration capabilities, allowing users to share notebooks with others and collaborate in real-time. This is particularly useful for research teams working on complex projects, as it allows team members to contribute their expertise and knowledge in real-time.

• Powerful computing resources: Google Colab provides access to powerful computing resources, such as GPUs and TPUs, which can greatly accelerate the training of machine learning models. This allows users to perform complex computations and analysis tasks quickly and efficiently.

• Interactive visualizations: Google Colab provides a range of tools for creating visualizations, such as interactive charts and graphs. This makes it easy for users to explore and analyze data in a visual format, which can help identify patterns and trends that might not be immediately apparent from the raw data.

• Security: Google Colab provides encryption for data in transit and at rest, as well as multi-factor authentication for added security. This ensures that user data is protected and secure at all times.

• Custom environments: Google Colab allows users to create custom environments with specific packages and dependencies, making it easy to reproduce experiments and share code with others.

• Easy deployment: Google Colab makes it easy to deploy machine learning models to production environments, such as Google Cloud Platform or TensorFlow Serving. This allows users to quickly and easily deploy their models and put them into use.

• Extensive documentation and tutorials: Google Colab provides extensive documentation and tutorials to help users get started with the platform and learn new skills. This makes it easy for users with different levels of experience to get up to speed quickly.

• Creating a Google Colab notebook

• Open the following URL in your browser − https://colab.research.google.com Your browser would display the following screen (assuming that you are logged into your Google Drive) −

• Create a new notebook: Click on the NEW PYTHON 3 NOTEBOOK link at the bottom of the screen. 

• A new notebook would open up as shown in the screen below.

• Name your notebook: Give your notebook a name by clicking on the "Untitled0" text at the top left corner of the page and typing a new name.

• Add code cells: To add code to your notebook, click on the "+" icon on the left-hand side of the page. This will create a new code cell where you can add your code.

• Add text cells: To add text to your notebook, click on the "+" icon on the left-hand side of the page and select "Text" from the drop-down menu. This will create a new text cell where you can add your text.

• Run code cells: To run your code, click on the "play" icon on the left-hand side of the code cell. Alternatively, you can use the keyboard shortcut "Shift+Enter" to run the code cell.

• Save your notebook: Google Colab automatically saves your notebook as you work on it, but you can also save it manually by clicking on "File" in the top left corner of the page and selecting "Save" from the drop-down menu.

• Share your notebook: To share your notebook with others, click on "Share" in the top right corner of the page. You can then choose to share the notebook with specific people or make it public.

• Download your notebook: To download your notebook, click on "File" in the top left corner of the page and select "Download .ipynb" from the drop-down menu. This will download your notebook as a Jupyter notebook file.

for more about Google Colab, you can go through some tutorial on youtube : Google Colab Tutorial for Beginners | Get Started with Google Colab

Deepnote is a new kind of data notebook that’s built for collaboration — Jupyter compatible, works magically in the cloud, and sharing is as easy as sending a link. 

You can think of Deepnote as the "Google Docs" of data science: it's a data notebook that allows for instantaneous collaboration through shared notebooks and workspaces as they say " a better way for teams to work with data". 

• Features

• • Jupyter-compatible

  • Programming languages: Jupyter languages (e.g. Python, R), SQL

  • Various Data Sources Integration

1. Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)

2. Connect to data warehouses (AWS, GCP, etc.)

3. Connect to databases (Postgres, MongoDB, etc.)

4. Provided file storage

• Powerful Data Visualization

1. Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)

2. UI for building charts

• Customize input cells, you can easily add text inputs, dates, and drop-downs to your notebooks 

• You can authorize Deepnote to access only selected (or all) repos of your GitHub organization. To learn more, check out the docs. 

• Portfolio Building with a Deepnote profile

• Deepnote apps: Deepnote allows you to transform your notebooks into interactive articles, dashboards and data apps in just a couple of clicks.

• Collaboration Workspaces are collaborative spaces where data teams can collaborate by setting up rights on the notebooks 

• Deepnote AI Copilot

• ChatGPT Integration for notebooks 

• If u r an instructor, give a look at this article on Medium: Deepnote: the modern way to teach Data Science

To stay updated with new features, visit Deepnote Announcements and Changelog

• Start your project

  • Open the Deepnote website: https://deepnote.com/. You will be prompted to sign in to your Deepnote account if you are not already signed in. 

  • In the interface, you can create a new folder for all your project data or a project you work on as shown below.

• Then, you will be directed to the following page. You can create a notebook, make an integration or uploud files to your project.

• Also, you can choose a prefered environment rather than Python 3.9 from the drop-down list below in Environment Section

• Open your notebook and start learning with us!

• Other Cloud Notebooks

Here are some Cloud notebooks that were not mentioned:

• • • Paperspace Gradient

    • Kaggle

    • Amazon SageMaker Studio Lab