5 Minutes of Fame

In the Czodrowski lab (www.czodrowskilab.org) group meeting, we started a session “5 Minutes of Fame”. Here, we update each other about any sort of (scientific/technological) news that might be of interest to the group.


AIDD School Feedback

Date: 2022_11_11
Author: Son Ha
Description: Recap of AIDD School, a series of talks organised by AIDD, an EU program which Son is a part of.


Date: 2022_11_04
Author: Paul Czodrowski
Description: gitbook

RDKit Feedback

Date: 2022_10_28
Author: Marcel Baltruschat, Aishvarya Tandon, Helge Vatheuer, Son Ha
Description: Highlights of 2022 RDKit UGM.

Clustering of Mpro fragment hits with SOM

Date: 2022-10-27
Author: Julien Hazemann
Description: Clustering of SARS-COV-2 Mpro Fragment Hits with a Self-Organizing Map.

Write (and read) configs with TOML

Date: 2022-10-07
Author: Aishvarya Tandon
Description: Quick introduction to TOML with hands-on tutorial. You will also learn how convenient it is to prepare and read log or config files made by TOML.

IPython Widgets and Voilà

Date: 2022-09-16
Author: Marcel Baltruschat
Description: This notebook shows how you can build interactive widgets in Jupyter notebooks and use Voilà to create a standalone web application out of it.


Date: 2022-09-09

Author: Anna Riede

Description: Academic Writing requires more than a spell check. Check out “Writefull” (https://www.writefull.com/) to improve your scientific papers / lab reports / …

GitHubCopilot meets RDKit

Date: 2022-08-12
Author: Helge Vatheuer
Description: Simple tasks of rdkit performed with the Copilot plugin inside VSCode – live demo is actually more impressive than the notebook.


Date: 2022-08-05
Author: Son Ha
Description: Quick and convenient way to set up a web interface for you machine learning model.

Quantum Mechanics

Date: 2022-07-22
Author: Paul Czodrowski
Description: Quantum mechanics and analytical chemistry courses ported to Jupyter notebooks. One more thing: Blog entry by Derek Lowe


Date: 2022-06-24
Author: Luca Kröll
Description: Short introduction to TikZ package in LaTeX and tikzplotlib tool for Python. Fascilitates making graphs and figures for LaTeX documents.

Decision Tree Classifier

Date: 2022-06-10
Author: Katharina Alker
Description: This notebook describes how to apply a DecisionTreeClassifier with the implemented algorithm of scikit-learn, which can be found on https://scikit-learn.org/stable/modules/tree.html. Including overtraining plot and visualisation of the important features using the example of morgan fingerprints.

(How to) Use Cohen’s kappa for your binary classification models!

Date: 2022-05-06
Author: Aishvarya Tandon
Description: This notebook introduces you to Cohen’s kappa and how to incorporate it with your binary classification model workflow. It’s use with k-fold cross-validation and Optuna hyperparameter optimization is also discussed.


Date: 2022-04-29
Author: Helge Vatheuer

OpenAI and Phrasebank

Date: 2022-04-08
Author: Son Ha
Description: 1) OpenAI Gym is a collection of Reinforcement Learning environment for you to train your RL algorithm. 2) Academic Phrasebank is a collection of academic sounding phrases you can use for your report/paper.

Track Optuna Hyperparameter Tuning with MLflow

Date: 2022-03-18
Author: Marcel Baltruschat
Description: This notebook shows how you can do hyperparameter tuning with Optuna and track the results with MLflow. It includes examples for automatic logging together with scikit-learn as well as manual logging with PyTorch.


Date: 2022-03-11
Author: Prof. Dr. Paul Czodrowski

CReM – chemically reasonable mutations

Date: 2022-02-25
Author: Julien Hazemann

OpenMM PyMol

Date: 2022-02-18
Author: Frederik Götz

Bunte Tüte

Date: 2022-02-10
Author: Helge Vatheuer

Quick intro to Optuna

Date: 2022-02-04
Author: Aishvarya Tandon
Description: This notebook introduces you to Optuna and provides cheminformatics examples for its usage.

GNN Interpretability

Date: 2022-01-28
Author: Marcel Baltruschat
Description: This notebook explores PyTorch Captum and GNNExplainer integrated in PyTorch Geometric to make Graph Neural Networks interpretable and more understandable. As an example, a GNN classification model for dividing compounds in two classes according to their mutagenic effect on a bacterium is examined.

Graph Classification with Graph Neural Networks

Date: 2022-01-21
Author: Son Ha
Description: This Notebook goes through how to construct a simple Graph Neural Network with PyTorch Geometric.

Dynamut Omicron Mpro with P132H mutation

Date: 2021-12-17
Author: Julien Hazemann


Date: 2021-12-17
Author: Julien Hazemann

Data Visualisation Catalogue

Date: 2021-11-12
Author: Juliana Gretz

Generating a SARS-CoV Mpro chemical space with Reinvent

Date: 2021-11-05
Author: Julien Hazemann

Regex Cheatsheet

Date: 2021-10-08
Author: Juliana Gretz

Pyro Showcase

Date: 2021-09-24
Author: Son Ha
Description: This notebook showcases some implementation of Pyro, a library built on PyTorch to facilitate your need for deep neural network statistical modeling.


Date: 2021-09-02
Author: Prof. Dr. Paul Czodrowski

Impact of chemical transformations on the hERG inhibition

Date: 2021-08-27
Author: Julien Hazemann

Load and write Pandas Dataframe faster, securely and efficiently with Parquet

Date: 2021-08-20
Author: Aishvarya Tandon
Description: In this notebook, I compare different dataframe saving and reading techniques, with the focus on parquet. I show that using parquet to save and read big dataframes has advantages over other methods.


Date: 2021-07-23
Author: Helge Vatheuer

Speed Up Cheminformatics

Date: 2021-07-16
Author: Marcel Baltruschat
Description: This notebook shows possibilities to speed up cheminformatics using NVIDIA® RAPIDS and Python Multiprocessing. This example includes exploring chemical space, calculating fingerprints, perform clustering or train machine learning models.

Covid-19 Moonshot

Date: 2021-07-09
Author: Prof. Dr. Paul Czodrowski

What’s new in Python 3.8 and 3.9

Date: 2021-02-04
Author: Marcel Baltruschat
Description: This notebook shows a quick summary of some interesting and useful new features introduced in Python 3.8 and 3.9.