Welcome to CHEM 5080: AI for Experimental Chemistry

Washington University in St. Louis
Fall 2026
Instructor: Zhiling “Zach” Zheng (he/him)
Office: McMillen 411 | Web: Deep Synthesis Lab | Email: z.z@wustl.edu

This class is open to everyone and can be taken at your own pace.

• All tutorials are free and shareable.

• Created for synthetic chemists with no background in coding or data science.

• Focuses on practical applications of AI in wet lab synthesis and experimental data analysis.

• Lecture slides are available to registered students on Canvas or upon request.

If you enjoy the course, please help spread the word and share the link so more chemists can benefit from it.

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Contents

• Lecture 1 - Python Primer
• Lecture 2 - Pandas and Plotting
• Lecture 3 - SMILES and RDKit
• Lecture 4 - Chemical Structure Identifier
• Lecture 5 - Regression and Classification
• Lecture 6 - Cross-Validation
• Lecture 7 - Decision Trees and Random Forests
• Lecture 8 - Neural Networks
• Lecture 9 - Graph Neural Networks

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Weekly Schedule

• Aug 25 & 27 – Course introduction + Python coding basics &

• Sept 1 & 3 – Molecular representations &

• Sept 8 & 10 – Classification & regression &

• Sept 15 & 17 – Supervised learning &

• Sept 22 & 24 – Molecular property & reaction prediction

• Sept 29 & Oct 1 – Unsupervised learning

• Oct 3–6 – Fall Break

• Oct 8 – Generative models in molecular design

• Oct 13 & 15 – Chemical reaction optimization

• Oct 20 & 22 – Semi-supervised learning

• Oct 27 & 29, Nov 3 – Transformers & large language models

• Nov 5 – Assignment 4 group work

• Nov 10 & 12 – Computer vision & multimodal models

• Nov 17, 19 & 24 – Multi-agent AI & literature data mining

• Nov 24 – Assignment 5 group work

• Nov 25–29 – Thanksgiving Break

• Dec 1 & 3 – Final project presentations & reflection

Assignments & Project Submissions (Canvas)

Date	Assignment / Project
Sept 6	Assignment 1 due
Sept 20	Assignment 2 due
Oct 4	Project 1 due
Oct 11	Project 1 Peer Review due
Oct 25	Assignment 3 due
Nov 8	Assignment 4 due
Nov 29	Assignment 5 due
Dec 6	Project 2 Presentation and Feedback due

Course Materials

• All resources provided via this Jupyter Book

• Exercises in Google Colab notebooks

• Recommended (not required) readings:

  • Introduction to Python Computations in Science and Engineering (Kitchin)

  • Machine Learning in Chemistry (Janet & Kulik)

  • Deep Learning for Molecules and Materials (White)

Learning Outcomes

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

• Select and apply ML techniques for chemical problems.

• Visualize and interpret chemical data.

• Implement code for reaction optimization.

• Use computer vision for chemical data.

• Experiment with generative and transformer-based models.

Grading

• Homework (40%): 5 assignments with coding, data analysis, and visualization tasks.

• Midterm Project (20%): Mini-review essay (ACS style).

• Final Project (30%): Team presentation and executive summary.

• Peer Review (10%): Structured feedback on classmates’ work.

• Bonus Points (up to 5%): In-class polls, questions, and discussions.

Academic Policies

• Integrity: Cite all sources (including AI tools).

• Recording: No unauthorized recording or distribution of course materials.

• Accommodations: Students needing accommodations should contact Disability Resources.

• Equity & Support: University policies on harassment, reporting, and academic resources apply.

Acknowledgements

We thank Professor Robert Wexler for his guidance in building this website and for helpful discussions on the course content.
We also acknowledge Bingcui Guo, whose contributions were tremendously valuable in discussing the material and in testing the code.

Cite as:
Z. Zheng. Developing an AI Course for Synthetic Chemistry Students. J. Chem. Educ. 103, 2621–2633, 2026.