CH-457 / 2 credits

Teacher: Schwaller Philippe

Language: English


Summary

The AI for Chemistry course will focus on teaching students how to use machine learning algorithms and techniques to analyze and make predictions about chemical data. The course will cover topics such as the basics of machine learning, common algorithms and their applications in chemistry.

Content

Keywords

articifial intelligence, machine learning, molecular design cycle, chemical reactions, data in chemistry, Python

Learning Prerequisites

Required courses

The course assumes basic programming knowledge, such as:

  • computer programming in Python (see Information, Computation, Communication CS-119(k))
  • https://www.kaggle.com/learn/python (another way to get familiar with basic Python programming)

Recommended courses

Machine learning for physicists (PHYS-467)

 

Important concepts to start the course

- A knowledge of Python programming and machine learning will be helpful, but the course is open to all.

 

Here some excellent resources:

- [Andrew White's deep learning for molecules & materials book](https://dmol.pub

- [MolSSI Education Resources](http://education.molssi.org/resources.html#programming)

- [Greg Landrum's RDKit blog](https://greglandrum.github.io/rdkit-blog/)

- [Esben Bjerrum's Cheminformania](https://www.cheminformania.com)

- [iwatobipens' blog](https://iwatobipen.wordpress.com)

- [Rocío Mercado's dl-chem-101](https://github.com/rociomer/dl-chem-101)

- [Jan H. Jensen's Machine Learning Basics](https://sites.google.com/view/ml-basics/home)

- [Pat Walter's Practical Cheminformatics With Open Source Software](https://github.com/PatWalters/practical_cheminformatics_tutorials)

Learning Outcomes

By the end of the course, the student must be able to:

  • Produce chemical data in a machine-readable format
  • Represent molecules and reactions in different chemical representations
  • Apply machine learning models to chemical tasks
  • Plan a machine learning project
  • Assess / Evaluate if machine learning models are suited for a given task

Transversal skills

  • Assess progress against the plan, and adapt the plan as appropriate.
  • Plan and carry out activities in a way which makes optimal use of available time and other resources.
  • Communicate effectively with professionals from other disciplines.
  • Demonstrate the capacity for critical thinking
  • Demonstrate a capacity for creativity.
  • Make an oral presentation.
  • Write a scientific or technical report.

Teaching methods

1h lecture

2h hands-on exercises (bring your own laptop)

Assessment methods

Final project : For the assessment, students will have the opportunity to apply the concepts and techniques they have learned throughout the course to a real-world problem in chemistry. This could be a research project, a case study, or a practical application of machine learning in chemistry. Students will work individually or in small groups to complete their projects, and will present their findings to the class in the last lecture and write a 4-page report.

 

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: AI for chemistry
  • Lecture: 1 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: AI for chemistry
  • Lecture: 1 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9    CE1104
9-10    CE1104
10-11    
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Friday, 8h - 9h: Lecture CE1104

Friday, 9h - 11h: Exercise, TP CE1104