Ersilia Book
  • 🤗Welcome to Ersilia!
    • The Ersilia Open Source Initiative
    • Ten principles
    • Ersilia's ecosystem
  • 🚀Ersilia Model Hub
    • Getting started
    • Online inference
    • Local inference
    • Model contribution
      • Model template
      • Model incorporation workflow
      • Troubleshooting models
      • BioModels annotation
    • For developers
      • Command line interface
      • CI/CD workflows
      • Test command
      • Testing playground
      • Model packaging
      • Inputs
      • Codebase quality and consistency
      • Results caching
  • đź’ŠChemistry tools
    • Automated activity prediction models
      • Light-weight AutoML with LazyQSAR
      • Accurate AutoML with ZairaChem
      • Model distillation with Olinda
    • Sampling the chemical space
    • Encryption of AI/ML models
  • AMR chemical collections
  • 🙌Contributors
    • Communication channels
    • Tech stack
    • Internships
      • Outreachy Summer 2025
      • Outreachy Winter 2024
      • Outreachy Summer 2024
      • Outreachy Winter 2023
      • Outreachy Summer 2023
      • Outreachy Winter 2022
      • Outreachy Summer 2022
  • đź“‘Training materials
    • AI2050 intro workshop
    • AI2050 AI for Drug Discovery
    • Introduction to ML for Drug Discovery
    • Python 101
    • External resources
  • 🎨Styles
    • Brand guidelines
    • Slide and document templates
    • Scientific figures with Stylia
    • Coding style
  • 🌍About Us
    • Where to find us?
    • Diversity and inclusion statement
    • Code of conduct
    • Open standards and best practices
    • Ersilia privacy notice
    • Strategic Plan 2025-2027
    • Ersilia, the Invisible City
Powered by GitBook

2025, Ersilia Open Source Initiative

On this page
  • Join our Slack community
  • Drug discovery materials
  • Wellcome Centre Anti-Infectives Research (WCAIR)
  • Literature reviews
  • Tools to get started in programming
  • Python
  • Google Colab

Was this helpful?

  1. Training materials

External resources

A few interesting learning resources related to drug discovery, machine learning, and global health

PreviousPython 101NextBrand guidelines

Last updated 11 months ago

Was this helpful?

Join our Slack community

There is an enormous amount of resources to learn about drug discovery, artificial intellicence & machine learning, and global health.

We continuously screen the literature, social media and the web in search of relevant content related these areas of research. Feel free to join our Slack community (message us at and we will send you an invitation link). In particular, you may be interested in the #literature and #technology and #media channels.

Drug discovery materials

Wellcome Centre Anti-Infectives Research (WCAIR)

The WCAIR in Dundee offers a set of free to support drug discovery research. We highly recommend the following:

Literature reviews

To get started with AI/ML applied to drug discovery, you can read these two review articles published in :

  • . "Rethinking drug design in the artificial intelligence era"

  • . "Applications of machine learning in drug discovery and development"

Tools to get started in programming

Python

Python is a popular beginner-friendly general-purpose programming language. It can be used for web development, automation, data analysis and machine learning. With an easy-to-read syntax, Python has easily become popular amongst scientists, accountants and programmers.

Google Colab

Google Colaboratory (Colab) is a Jupyter notebook that allows users to write and execute Python code for free in Google cloud. At Ersilia, we develop our lessons in Colab because it does not require any installation on the student's local server and it uses the computational capacity of Google cloud, removing any requirements for the student’s computers.

Colab's free plan allows for 12 hours of runtime and 16GB of storage. These features are more than sufficient for the purposes of training and education. In addition, Colab can allocate GPU computing time which helps in machine learning by processing large amounts of data in a short time.

How to access Colab

Colab Sessions

Running Colab Notebooks

Colab is an online Jupyter Notebook. Jupyter notebooks are the most widely interactive platform for computing. They are composed of individual cells, each one containing a piece of code that can be executed independently from the others. In Colab, you can click on the left of each cell the “play” button to execute the code in this cell.

Importing files to Colab

Many times we need to import files, such as an excel table, into our Colab notebook. There are several ways to do so, here we explain in detail two of them:

By mounting Google Drive

You can access files in your Google Drive by mounting your drive in Colab’s runtime virtual machine

  • Import drive and use the .mount function

from google.colab import drive
drive.mount('/content/drive')

When you run the above command, an authentication page will come up seeking your permission to allow Colab access files in your drive. Once you have approved, you should get the output that reads, “Mounted at /content/drive”

  • Once the drive is mounted, the folder icon on the left panel of the Colab page will open your drive hierarchy. Locate the file of interest in drive and copy the file path (right click → copy path).

  • Load the file in Colab using the copied path. For example, to import a .csv file into a Pandas Dataframe, you would use the following code:Use the code below to read the file (assuming it is an Excel file in .csv format) using read_csv() and the copied file path pasted in the parentheses

import pandas as pd
df = pd.read_csv("/content/drive/MyDrive/data/excel_file.csv")

If the file is imported successfully, there will be no error message.

By importing files from Github

  • Copy the URL of the .csv file hosted on Github. The URL should link to the file in raw format.

  • Paste the link and import the file.

url = 'copied_csv_file_in_raw_form_Github_link'
df = pd.read_csv(url)

If the file import is successful, there will be no error message.

The only requisite to access Colab is to have a Google Account. If you don’t have an account in Google, you can to create one. Next, head to and install the extension for your google account (free and only required the first time that Colab is used). Colab notebooks are stored on your Google Drive. To create a new notebook, go to the desired folder in your Drive, right click and select More > Colab.

đź“‘
hello@ersilia.io
online courses
Medicinal Chemistry
The Basics of DMPK
In Conversation
Nature Reviews Drug Discovery
Schneider et al. (2020)
Vamathevan et al. (2019)
sign up
Colab