Machine learning methods for drug discovery and toxicology: How to develop a QSAR model using Python

This online course allows you to learn at your own pace. The course remains accessible at all times, giving you the flexibility to register whenever you choose. Once enrolled, you’ll have a four-month window to complete the course. In our platform, you will have access to recorded lessons, text explanations, interactive Python exercises, and other resources. This is a practical course with several practical exercises that will finish with a project when you will develop a whole QSAR model by yourself. But you are not alone; a tutor from ProtoQSAR will follow your advance on the platform, give you feedback on your assignments and will be available by e-mail. Additionally, you will be able to interact with your colleagues and instructors through internal forums and chats, and it will be a series of live videoconferences to clarify doubts.

For more details, please download the course content.

Prerequisites: The tasks require a basic knowledge of Python (additional learning resources and links to external resources will be available in the platform for begginers).

Mode: Online Pre-registration: Fill in this form
When? Always open (4 months to complete) Information:
Estimated hours: 60 Course language: English
Price: 480 €* * 280€ for students (requisites in form)

Course overview:

Introduction of basic concepts
  • Overview of different computational approaches
  • QSAR model workflow
  • Regression vs Classification
  • Statistical analysis of models
 Python basic techniques for chemoinformatics
  • Dataframe importation and analysis
  • Molecule characterization
  • Chemical and biological data curation
QSAR model development
  • Data compilation and curation
  • Calculation of molecular descriptors
  • Train/test splitting
  • Feature reduction and scaling
  • Algorithm selection
  • Hyperparameter optimization
  • Model metrics and validation
  • Applicability domain
  • External prediction