Online tutorial

Machine learning in drug discovery

A practical introduction

Time to complete:

3 hours

This course includes:

  • Activities

Written by:

  • Denise Carvalho-Silva
  • Arwa Raies

Last reviewed:

January 2025


Creative Commons

All materials are free cultural works licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, except where further licensing details are provided.


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What is machine learning and how can it be applied in drug discovery to identify or prioritise new drug targets? Find out more with this introduction to machine learning applications in drug discovery using WEKA.

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Who is this course for?

This course is aimed at scientists with no previous experience of machine learning (ML) and who are interested in the applications of ML in drug discovery. The course will provide a broad overview of how to develop machine learning models without programming using WEKA. You will also learn how to interpret some of the results returned from the WEKA practicals.

No experience in machine learning or drug discovery pipeline is required but undergraduate knowledge of a subject related to the life sciences would be an advantage.

What will I achieve?

By the end of the course you will be able to:

  • Identify common types of ML algorithms that can be applied to tackle drug discovery challenges
  • Illustrate some applications of machine learning and other artificial intelligence frameworks in drug discovery
  • Get started with WEKA, an easy-to-use open-source machine learning software
  • Use standalone web resources to explore the WEKA results and see if the identified genes could be potential targets in drug discovery

What resources do I need?

You will need to download the open-source machine learning software WEKA: https://waikato.github.io/weka-wiki/downloading_weka/

DOI: 10.6019/TOL.MLDrugDisc-t.2021.00001.1