kochgroup/intro_pharma_ai

A collection of Jupyter Notebooks that are designed to teach life science students about deep learning.

Welcome to:
"Introduction to Artificial Intelligence for Life Science Students"

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
06. Linear Algebra
07. Your first Neural Network
08. PyTorch
09. Convolutional Neural Network
10. Transfer Learning
11. Recurrent Neural Networks
12. Autoencoders
13. Graph Neural Networks
14. Summary

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

TeachOpenCADD A collection of notebooks covering a wide range of topics related to cheminformatics and data science, like collecting and cleaning molecular data in Python, but also more advanced topics like Docking.

intro_pharma_ai's Issues

Dead link to image in Notebook 10 - Transfer Learning (EN & GER)

Dead link:
https://pennylane.ai/qml/_images/transfer_learning_general.png

What exactly was displayed here?

for function onehotencode().

hi, thanks a lot to provide such good materials for learning.
i have a question about setion 13, as mentioned,
For the one-hot encoding of the atoms we use the already written function onehotencode().

but i cannot find the function in other sections. could you please provide more detailed codes for this function?
many thanks,

best,

Add GPU support to Week 12 - Autoencoders

We need to add GPU support to Week 12 Autoencoders

Missing "In this section you'll learn" in multiple notebooks (EN & GER)

We should add the section, so advanced users can skip certain notebooks / skip to certain sections.
Also rename the section (either "Learning Objectives" or "In this section you'll learn".

Notebook	EN	GER ("Lernziele")
01 - Introduction to Jupyter	missing	missing
02 - Introduction to Python	missing	missing
03 - Cheminformatics
04 - Linear Regression
05 - Data Science
06 - Linear Algebra for NN
07 - First Neural Net
08 - PyTorch
09 - Convolutional Neural Network	missing	missing
10 - Transfer Learning
11 - Recurrent Neural Networks	missing	missing
12 - Autoencoders	called "Learning Objectives"
13 - Graph Neural Networks	called "Learning Objectives"
14 - Summary	called "Learning Objectives"

Remove line "submit for grading"

At least notebooks 03 and 04 (english) still have a line in the practice exercise asking to submit the solution.

Found some German inside the notebooks

Hey, very nice notebooks you have.
I enjoyed a lot, but occasionally I find some German inside the English version. I will add the screenshots here, so you would be able to fix it some day:)
Many thanks for your work!

Check if correction of mistake is also necessary in german translation.

1283559 and 5e4292d are the commits in question.