This repository contains a collection of Jupyter Notebooks, which can be used to teach pharmaceutical and chemistry students the basics of Deep Learning. No prior coding knowledge is required. The article introducing this repository can be found here: https://doi.org/10.1002/ardp.202200628 and was written by Janosch Menke, Samuel Homberg and Oliver Koch.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This work was funded by the "Apotheker Stiftung Westfalen-Lippe"

Usage

1. Goolge Colab The easiest way to use the Notebooks is to open them in Google Colab. The only thing needed is a Google Account. You can open a Juypter Notebook by simply clicking on a button in the table below. All notebooks will work out-of-the-box.

2. Local Installation If you do not want to run the notebooks through a Google service, you can also setup your own local Python environment. We provide an instruction on how to do this. Like with Colab all notebooks will work straight away, as soon as the local installation has been completed.

Notebook	English	German
01. Introduction to Jupyter
02. Introduction to Python
03. Cheminformatics & RDKit
04. Linear Regression
05. Data Science

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New Notebooks

Notebook	English
Math Concepts (Linear Algebra + Regression)
Transformer
REINVENT4-DeNovoDesign
REINVENT4-Libinvent
Co-folding

We want to point out that these notebooks are, on their own, not sufficient to properly convey the knowledge and teach students about deep learning. Instructors need to prepare their own accompanying lectures. It is also important to mention that these notebooks are not designed to bring students to a level where they are able to train neural networks without any aid. Rather, the notebooks are designed to teach students the theoretical concepts to understand neural networks through code completion. We believe, as explained in more detail in the paper, that the theory bheind neural networks is easy to understand. But learning about them, is difficult as it requieres a solid understanding of a programming language. So students would get stuck on syntactical problems posed by the programming language rather than the theory behind neural networks.

Contribution or Expensions

We hope that these notebooks can be a starting point for others to expand on or contribute to. Everyone is free to adapt this repository (in accoradance with the above mentioned license).

Data Sources

Name	Source
MNIST	LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
BBBP	Martins, I. F., et al. (2012) A Bayesian approach to in silico blood-brain barrier penetration modeling. Journal of Chemical Information and Modeling, 52(6), 1686-1697.
Pneumonia	Kermany, D., Zhang, K., Goldbaum, M. (2018), Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images, Mendeley Data, V3, doi: 10.17632/rscbjbr9sj.3 Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., ... & Zhang, K. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131.
Cats & Dogs	Parkhi, O. M., Vedaldi, A., Zisserman, A., & Jawahar, C. V. (2012). Cats and dogs. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3498-3505). IEEE.
GDB 11	Fink, T., & Reymond, J. L. (2007). Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. Journal of Chemical Information and Modeling, 47(2), 342-353.

Additional Information

ImageNet Background

Further Instructional Materials

[TeachOpe