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awesome-learning-digital-chemistry

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a curated list of resources for everyone interested in learning about digital chemistry

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# awesome-learning-digital-chemistry [![Awesome](https://awesome.re/badge-flat2.svg)](https://awesome.re)
You are welcome to contribute to this project! See [CONTRIBUTING.md](CONTRIBUTING.md) for details.

**Table of content:**
- [Foundational Knowledge](#foundations)
- [Core Topics in Digital Chemistry](#core-topics)
- [Courses](#courses)
- [Tutorials](#tutorials)
- [Blogs and Articles](#blogs)
- [Communities and Resources](#communities)
- [Related Awesome Lists](#related)

<a id="foundations"></a>
## Foundational Knowledge

### Getting Started: Computer Science & Machine Learning Basics
* [Neural Networks: Zero to Hero](https://github.com/karpathy/nn-zero-to-hero) – A Tutorial on the basics of Neural Networks and NLP
* [The Turing Way](https://github.com/the-turing-way/the-turing-way) – Resources on Data Managment/Reproducible Science

### Programming for Chemists
* [Introduction to version control for scientists](https://laserkelvin.github.io/blog/2021/10/contributing-github/)
* [Scientific Computing for Chemists](https://weisscharlesj.github.io/SciCompforChemists/notebooks/introduction/intro.html)
* [MolSSI Education: Python Scripting CMS](https://github.com/MolSSI-Education/python_scripting_cms)
* [Deep Learning for Chemistry 101](https://github.com/rociomer/dl-chem-101)

<a id="core-topics"></a>
## Core Topics in Digital Chemistry

### Cheminformatics
* [Sulstice Cheminformatics Teaching Material](https://github.com/Sulstice/Cheminformatics-Teaching-Material)

### Machine Learning and Materials Informatics
* [Sp8rks Materials Informatics](https://github.com/sp8rks/MaterialsInformatics)
* [ML for Materials](https://github.com/aronwalsh/MLforMaterials)

### Self-Driving Labs
* [Self Driving Labs: A Demo](https://github.com/sparks-baird/self-driving-lab-demo)

<a id="courses"></a>
## Courses

* [AI4Chemistry course](https://github.com/schwallergroup/ai4chem_course) - The Artificial Intelligence (AI) for Chemistry taught in Spring 2023 at EPFL (CH-457). It is a course with a lot of hands-on exercises. Experience in Python programming and machine learning (ML) will help you to get up to speed quickly.
* [Practical Programming](https://schwallergroup.github.io/practical-programming-in-chemistry/) – 
This course offers a thorough introduction to programming for chemists and chemical engineers using Python, covering fundamental concepts and tools relevant to chemical tasks, from Git to the RDKit. The exercises are freely accessible.
* [Machine Learning for Materials](https://aronwalsh.github.io/MLforMaterials/Overview.html) – An introduction to statistical research tools for materials theory and simulation at the Department of Materials at ICL.
* [Introduction to Cheminformatics and AI in Drug Discovery: Hands-on Modeling of Safety Data](https://srijitseal.com/AIDDCourse) – Machine learning (ML) and artificial intelligence (AI) models are becoming increasingly popular in drug discovery. This course aims to explain how to construct and use ML models for a non-expert audience with a background in life science or safety sciences.
* [CHEM 5080: AI for Experimental Chemistry](https://zzhenglab.github.io/ai4chem/intro.html) – This course is taught at the Washington University in St. Louis. It is created for synthetic chemists with no background in coding or data science, and 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.

<a id="tutorials"></a>
## Tutorials

* [VolkamerLab Talktorials on CADD](https://projects.volkamerlab.org/teachopencadd/talktorials.html) – "Talktorials" (portmanteau for talk and tutorial) address many topics central to computer-aided drug design. Topics range from cheminformatics, online queries to structural biology.
* [Practical Cheminformatic Tutorials by Patrick Walters](https://github.com/PatWalters/practical_cheminformatics_tutorials) – Jupyter notebooks and Google colab notebooks for learning Cheminformatics. From fundamentals of concepts in cheminformatics over clustering, SAR analysis, machine learning and active learning: This course covers everything!
* [Transformers for Chemistry and Materials Science](https://github.com/lamalab-org/llm-tutorial) – A collection of the [LlamaLab](https://github.com/lamalab-org)'s tutorials on transformers in chemistry and materials science, adapted from blogposts from [Kevin M. Jablonka](https://kjablonka.com/index.html#category=llm)'s Blog.
* [Official code tutorial for ACS In Focus (2024) - Neural Networks for Chemists](https://github.com/omics101/acs-neural-networks) – a collection of first-step knowledge and tools to begin harnessing the power of neural networks in your own work.

<a id="blogs"></a>
## Blogs and Articles
* [The Valence Kjell](https://www.valencekjell.com/) – A blog about computational chemistry, cheminformatics and machine learning
* [Practical Cheminformatics](https://practicalcheminformatics.blogspot.com/) ([here](https://patwalters.github.io/) since April 2025) – A blog by Pat Walters about many topics in cheminformatics, among others, generative models, LLMs and machine learning in drug discovery.
* [Byte Sized Chemistry](https://bytesizedchem.com) – A blog about digital chemistry, data visualization and student life

*Do you want a Digital Chemistry blog to be featured? Let us know by submitting a pull request!*

<a id="communities"></a>
## Communities and Resources

* [OpenBioML](https://www.openbioml.org/) - OpenBioML is a decentralized, collaborative research community founded on the belief that open source machine learning and open science can accelerate biotechnology.
* [LeMaterial](https://lematerial.org/) – LeMaterial is an open-source collaborative project designed to simplify and accelerate materials research for scientists and ML practionners. By joining the slack and monthly community meetings, you are able to contribute to working groups for large language models, generative models and benchmarks in materials science.

<a id="related"></a>
## Related Awesome Lists
* [Awesome Chemical Engineering Education](https://github.com/kevingreenman/awesome-chemical-engineering-education)
* [Awesome Scientific Language Models](https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models)
* [Awesome Chemistry Datasets](https://github.com/kjappelbaum/awesome-chemistry-datasets)
* [Awesome Python Chemistry Tools](https://github.com/lmmentel/awesome-python-chemistry)

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